A method for detecting water ice in a lunar permanent shadow region by infrared remote sensing based on spectral information correction

Abstract

The present application relates to a kind of lunar permanent shadow area water ice infrared remote sensing detection method based on spectral information correction, comprising the following steps: step 1) permanent shadow area pixel periphery scattering source position is determined by joint field of view analysis and illumination range;Step 2) solve the irradiance of scattering into the location of permanent shadow area pixel;Step 3) satellite sensor observation value is compared with the solved scattering irradiance, and band is carried out ratio operation, and the corrected infrared spectral reflectance is obtained;Step 4) the normalized processing and adaptive fitting of corrected spectral reflectance are carried out, and the spectral curve is obtained;Step 5) the comprehensive determination criterion of water ice existence is constructed, and the location of water ice is determined based on spectral curve.Compared with prior art, the present application has the advantage that detection reliability is higher.

Description

Technical Field

[0001] This invention relates to lunar water ice remote sensing technology, and in particular to an infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction. Background Technology

[0002] Lunar water ice holds significant engineering and scientific value. It not only promises to provide water resources for future lunar base construction but also offers crucial information for a deeper understanding of the origin and evolution of the solar system. Currently, numerous countries have announced plans to conduct lunar water ice exploration in the near future. Due to the relatively small tilt of the Moon's axial tilt relative to its orbital plane, the lunar polar regions contain areas without direct sunlight, forming permanently shadowed zones. These permanently shadowed zones have extremely low temperatures, potentially leading to the accumulation of large amounts of water ice and other volatile compounds. The water resource potential of the permanently shadowed regions in the lunar poles makes them a key area for water ice exploration.

[0003] Remote sensing has long been the primary means of detecting water ice on the Moon, encompassing data types such as neutron, radar, and infrared. Thermal neutron counting can accurately detect the hydrogen content and distribution at the lunar south pole, but its resolution is generally low. Radar echo polarization signals contain water ice information, but are significantly affected by lunar surface roughness, leading to considerable controversy regarding the detection results. Water ice exhibits clear absorption of the infrared spectrum across multiple frequency bands, forming unique spectral curves. This gives infrared remote sensing data a unique advantage in water ice detection, making it an important tool for lunar water ice remote sensing. However, permanently shadowed regions lack direct sunlight, and the infrared spectral information received by sensors originates from reflections from surrounding terrain features, making it difficult to directly use for water ice detection and analysis. Previous studies have proposed correcting the spectral information of permanently shadowed regions by dividing it by the average reflectance of sunlit areas in the lunar polar regions. While this global average correction method is simple and easy to implement, it does not consider the influence of local topography, illumination, and lunar regolith physical properties on scattered light. Furthermore, the signal-to-noise ratio of spectral data from permanently shadowed regions is low, and noise causes drastic fluctuations in the spectral domain. These fluctuations can mask the actual spectral characteristics, and the degree of fluctuation in spectral data acquired in different regions and at different times also varies, increasing the uncertainty in extracting water ice information. Summary of the Invention

[0004] The purpose of this invention is to provide an infrared remote sensing method for detecting water ice in the permanently shadowed region of the moon based on spectral information correction, which suppresses the interference of surrounding scattering source information and random spectral fluctuations on spectral data, and achieves effective detection of water ice signals based on restoring the spectral morphology of the permanently shadowed region.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction includes the following steps:

[0007] Step 1) Combine field-of-view analysis and illumination range analysis to determine the location of scattering sources around pixels in the permanent shadow area;

[0008] Step 2) Solve for the irradiance at the location of the pixel scattered into the permanent shadow region;

[0009] Step 3) Calculate the ratio between the satellite sensor observations and the calculated scattered irradiance band by band to obtain the corrected infrared spectral reflectance;

[0010] Step 4) Normalize the corrected spectral reflectance and perform adaptive fitting to obtain the spectral curve;

[0011] Step 5) Construct a comprehensive criterion for determining the existence of water ice, and determine the location of water ice based on the spectral curve.

[0012] Step 1) includes the following steps:

[0013] Step 1-1) For any pixel within the permanently shadowed area, obtain the surrounding visible area based on the lunar surface topography using field-of-view analysis;

[0014] Steps 1-2) Extract the shooting time of the infrared spectral data of the location of the permanent shadowed area pixels, and obtain the range of the sun's direct rays at that moment based on ephemeris information and lunar surface topography;

[0015] Steps 1-3) Intersect the visible area around the permanent shadowed pixel with the area directly exposed to the sun to obtain the distribution area and number of the surrounding scattering source pixels.

[0016] Step 2) includes the following steps:

[0017] Step 2-1) For any scattering source pixel found around a pixel in the permanent shadow region, let the pixel in the permanent shadow region be pixel P and the scattering source pixel be pixel S. Calculate the spectral reflectance from pixel S to pixel P using the Hapke model:

[0018]

[0019] Where r(λ,i,e,g) is the spectral reflectance of the λ band, i is the solar incidence angle at pixel S, e is the emission angle, and g is the phase angle; μ0 is the cosine of the incidence angle i, and μ is the cosine of the emission angle e; B(g) and P(g) are the backscattering function and phase function, respectively, and H(μ0,ω) and H(μ,ω) are functions describing the multiple scattering process; ω(λ) is the single-scattering albedo of the λ band at pixel S.

[0020] Step 2-2) Using the solved spectral reflectance, calculate the irradiance scattered from pixel S into pixel P:

[0021] I scatter (λ)=τ×I sun (λ)×r(λ,i,e,g)

[0022] Among them, I scatter (λ) is the scattered irradiance corresponding to the λ band, I sun (λ) is the spectral irradiance of direct solar radiation in the λ band, and τ is the visibility of the solar disk at pixel S.

[0023] Step 3) includes the following steps:

[0024] Step 3-1) Solve for each surrounding scattering source pixel S1, S2, ..., S n Irradiance scattered into the permanent shadow region of pixel P Where n is the number of scattering source pixels;

[0025] Step 3-2) Calculate the total scattered irradiance received by the pixels in the permanent shadow region:

[0026]

[0027] in, It is the total received scattered irradiance corresponding to the λ band. i is the irradiance of pixel P, which is scattered into the permanent shadow region by the kth scattering source pixel corresponding to the λ band. k Ω is the angle of incidence of the scattered ray at pixel P. k For the projected solid angle;

[0028] Step 3-3) Obtain the corrected spectral reflectance by calculating the ratio between irradiance values:

[0029]

[0030] Where R(λ) is the spectral reflectance after correction for the λ band, and I(λ) is the spectral irradiance recorded in the infrared spectral data corresponding to the λ band.

[0031] Step 4) includes the following steps:

[0032] Step 4-1) Normalize the spectral reflectance of the pixels in the permanent shadow area after correction:

[0033]

[0034] Among them, R Norm R(λ) is the normalized spectral reflectance corresponding to the λ band, and R(λ) is the corrected spectral reflectance corresponding to the λ band.Min and R Max These represent the minimum and maximum spectral reflectance within the research band, respectively.

[0035] Step 4-2) Use the CSAPS method to perform adaptive smoothing fitting on the normalized spectral reflectance to obtain the fitted spectral curve.

[0036] The research band range is 1.0-2.5μm.

[0037] Step 5) includes the following steps:

[0038] Step 5-1) For pixels in the permanent shaded area, use spectral continuum analysis to find the center position of the absorption band on the fitted spectral curve;

[0039] Step 5-2) Absorption feature judgment: Compare the center position of the found absorption spectrum with the known water ice absorption spectrum. If the spectral absorption center is found in the known water ice absorption spectrum and the absorption feature is obvious, that is, the absorption intensity is greater than the preset threshold, then the permanent shadow area pixel is considered to be a potential water ice pixel.

[0040] Step 5-3) Spectral similarity analysis: For the detected potential water ice pixels, evaluate the degree of similarity between them and the spectrum of pure water ice in the laboratory;

[0041] Step 5-4) Extract potential water ice pixels that meet the preset conditions in terms of similarity to the spectrum of laboratory pure water ice, and mark them as the finally detected water ice pixels.

[0042] In step 5-3), the evaluation indicators for assessing the similarity between the spectrum of the spectrum and that of laboratory pure water ice include spectral angle and spectral information divergence.

[0043] The methods for calculating the spectral angle and spectral information divergence are as follows:

[0044]

[0045] in, Refers to the spectral reflectance vector of potential water ice pixels in the permanently shaded region; The spectral reflectance vector of laboratory-grade pure water ice; m i Let w be the spectral reflectance of the i-th potential water ice pixel in the permanent shadow region. i Let be the spectral reflectance of the i-th laboratory pure water ice, and n be the number of spectral reflectance values.

[0046] In step 5-4), when the spectral angle corresponding to the potential water ice pixel is <30° and the spectral information divergence is <8, it is marked as the finally detected water ice pixel.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) Spectral data of permanently shadowed regions are affected by signals from surrounding scattering sources. Existing methods correct the spectral information of permanently shadowed regions by dividing it by the average reflectance of sunlit areas in the lunar polar regions. While this global average correction method is simple and easy to implement, it ignores the differences in spectral imaging conditions of pixels in permanently shadowed regions. This invention constructs a separate model for each pixel in a permanently shadowed region for correction, taking into account factors such as spectral bands, topography, geometry, and physical properties, which helps to improve the reliability of water ice detection results.

[0049] (2) Fluctuations in spectral data in permanently shaded regions can mask the actual absorption characteristics of water ice, making it difficult to extract water ice information. Existing methods often extract water ice information by fitting spectral curves, but these methods and models fail to comprehensively consider the overall distribution and local variations of the spectral curves, easily leading to omissions or overestimations of water ice absorption signals. This invention specifically normalizes and adaptively fits the spectral data to suppress the masking effect of noise-induced fluctuations in spectral data on water ice information. Based on this, a comprehensive criterion for determining the existence of water ice, integrating spectral absorption characteristics and spectral similarity, is constructed, which helps extract effective water ice signals from interfered spectral signals. Attached Figure Description

[0050] Figure 1 This is a flowchart of the method of the present invention;

[0051] Figure 2 A schematic diagram illustrating the method for locating scattering sources around a permanently shadowed area;

[0052] Figure 3 This is a schematic diagram showing the number of scattering source pixels and scattered irradiance around a permanent shadow area in one embodiment.

[0053] Figure 4 This is a schematic diagram of the spectral data and fitting curve before and after correction in one embodiment;

[0054] Figure 5 This is a schematic diagram illustrating the changes in the detected water ice pixels and their number as a function of parameters in one embodiment. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0056] Due to the shortcomings of existing technologies, researching methods to correct infrared reflectance spectral information in permanently shadowed regions of the moon and suppressing the impact of noise-induced spectral data fluctuations on lunar surface water ice information is of great significance for improving the reliability of infrared remote sensing results for water ice.

[0057] Based on this, this embodiment provides an infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction, such as... Figure 1 As shown, it includes the following steps:

[0058] Step 1) Combine field of view analysis and illumination range to determine the location of scattering sources around pixels in the permanent shadow area.

[0059] Specifically, step 1) includes the following steps:

[0060] Step 1-1) For any pixel within the permanently shadowed area, obtain the surrounding visible area based on the lunar surface topography using field-of-view analysis;

[0061] Steps 1-2) Extract the shooting time of the infrared spectral data of the location of the permanent shadowed area pixels, and obtain the range of the sun's direct rays at that moment based on ephemeris information and lunar surface topography;

[0062] Steps 1-3) Intersect the visible area around the permanent shadowed pixel with the area directly exposed to the sun to obtain the distribution area and number of the surrounding scattering source pixels.

[0063] Figure 2 The distribution of scattering source pixels obtained from the above steps is illustrated through an example. It can be seen that pixels in the permanently shaded area are distributed in low-lying areas with small surrounding visible areas, generally located at the edge of the impact crater. There is overlap between the sunlit area and the surrounding visible area; the location of scattering source pixels surrounding the permanently shaded area can be obtained by taking the intersection.

[0064] Step 2) Solve for the irradiance at the location of the pixel that is scattered into the permanent shadow area.

[0065] Specifically, step 2) includes the following steps:

[0066] Step 2-1) For any scattering source pixel found around a pixel in the permanent shadow region, let the pixel in the permanent shadow region be pixel P and the scattering source pixel be pixel S. Calculate the spectral reflectance from pixel S to pixel P using the Hapke model:

[0067]

[0068] Where r(λ,i,e,g) is the spectral reflectance of the λ band, i is the solar incidence angle at pixel S, e is the emission angle, and g is the phase angle; μ0 is the cosine of the incidence angle i, and μ is the cosine of the emission angle e; B(g) and P(g) are the backscattering function and phase function, respectively, and H(μ0,ω) and H(μ,ω) are functions describing the multiple scattering process; ω(λ) is the single-scattering albedo of the λ band at pixel S.

[0069] Step 2-2) Using the solved spectral reflectance, calculate the irradiance scattered from pixel S into pixel P:

[0070] I scatter (λ)=τ×I sun (λ)×r(λ,i,e,g)

[0071] Among them, I scatter (λ) is the scattered irradiance corresponding to the λ band, I sun (λ) is the spectral irradiance of direct solar radiation in the λ band, and τ is the visibility of the solar disk at pixel S.

[0072] Step 3) Calculate the ratio between the satellite sensor observations and the solved scattering irradiance band by band to obtain the corrected infrared spectral reflectance.

[0073] Specifically, step 3) includes the following steps:

[0074] Step 3-1) Solve for each surrounding scattering source pixel S1, S2, ..., S n Irradiance scattered into the permanent shadow region of pixel P Where n is the number of scattering source pixels;

[0075] Step 3-2) Calculate the total scattered irradiance received by the pixels in the permanent shadow region:

[0076]

[0077] in, It is the total received scattered irradiance corresponding to the λ band. i is the irradiance of pixel P, which is scattered into the permanent shadow region by the kth scattering source pixel corresponding to the λ band. k Ω is the angle of incidence of the scattered ray at pixel P. k For the projected solid angle;

[0078] Step 3-3) Obtain the corrected spectral reflectance by calculating the ratio between irradiance values:

[0079]

[0080] Where R(λ) is the spectral reflectance after correction for the λ band, and I(λ) is the spectral irradiance recorded in the infrared spectral data corresponding to the λ band.

[0081] Figure 3 This study demonstrates the number of scattering source pixels and scattered irradiance surrounding pixels in the permanently shadowed areas within the Nobilli crater. The number of these peripheral scattering source pixels exhibits significant spatial differences. The terrain in the central region of the Nobilli crater is lower than the surrounding areas, making it difficult for pixels in the permanently shadowed areas to form a line-of-sight path with the illuminated areas. Therefore, the number of peripheral scattering source pixels found by pixels in the permanently shadowed areas within the Nobilli crater varies spatially, generally exhibiting a spatial distribution characteristic where the central region of the permanently shadowed area is lower than the surrounding areas. The spatial distribution of scattered irradiance received by the permanently shadowed areas within the Nobilli crater is similar to the number of peripheral scattering source pixels. Pixels in permanently shadowed areas with a large number of peripheral scattering source pixels typically have higher scattered irradiance. However, these two spatial distributions are not entirely identical. This is because scattered irradiance is also influenced by factors such as the sun's position, local topography, and the physical properties of the lunar regolith. The scattered irradiance data allows observation of surface texture changes in the permanently shadowed areas, providing evidence for the reliability of the results.

[0082] Step 4) Normalize and adaptively fit the corrected spectral reflectance to obtain the spectral curve, reduce spectral noise interference, and highlight the spectral curve variation characteristics.

[0083] Specifically, step 4) includes the following steps:

[0084] Step 4-1) Normalize the spectral reflectance of the pixels in the permanent shadow area after correction:

[0085]

[0086] Among them, R Norm R(λ) is the normalized spectral reflectance corresponding to the λ band, and R(λ) is the corrected spectral reflectance corresponding to the λ band. Min and R Max These represent the minimum and maximum spectral reflectance within the study wavelength range (1.0-2.5 μm), respectively.

[0087] Step 4-2) Use the CSAPS method to perform adaptive smoothing fitting on the normalized spectral reflectance to obtain the fitted spectral curve, suppressing the interference of random fluctuations in the data caused by noise and highlighting the variation characteristics of the spectral curve.

[0088] Figure 4The normalized spectral reflectance and fitting curves before and after correction are shown. It can be seen that the overall morphology of the spectrum has changed significantly after correction, and the severe local fluctuations in the spectra of some pixels have been improved. While the spectral morphology of some pixels before correction differs considerably from that of laboratory pure water ice, the spectral morphology of these pixels after correction shows a higher degree of similarity to that of laboratory pure water ice.

[0089] Step 5) Construct a comprehensive criterion for determining the existence of water ice, and determine the location of water ice based on the spectral curve.

[0090] Specifically, step 5) includes the following steps:

[0091] Step 5-1) For pixels in the permanent shaded area, use spectral continuum analysis to find the center position of the absorption band on the fitted spectral curve;

[0092] Step 5-2) Absorption feature judgment: Compare the center position of the found absorption spectrum with the known water ice absorption spectrum (around 1.3μm, 1.5μm and 2.0μm). If the spectral absorption center is found in all known water ice absorption spectrum ranges and the absorption feature is obvious, that is, the absorption intensity is greater than 10%, then the pixel in the permanent shadow area is considered to be a potential water ice pixel.

[0093] Step 5-3) Spectral similarity analysis: For the detected potential water ice pixels, evaluate the degree of similarity between them and the spectrum of pure water ice in the laboratory; in this embodiment, the evaluation indicators include spectral angle and spectral information divergence:

[0094]

[0095] in, Refers to the spectral reflectance vector of potential water ice pixels in the permanently shaded region; The spectral reflectance vector of laboratory-grade pure water ice; m i Let w be the spectral reflectance of the i-th potential water ice pixel in the permanent shadow region. i Let be the spectral reflectance of the i-th laboratory pure water ice, and n be the number of spectral reflectance values.

[0096] Step 5-4) Extract potential water ice pixels that meet the preset conditions (spectral angle <30° and spectral information divergence <8) with the same spectral similarity to laboratory pure water ice, and mark them as the finally detected water ice pixels.

[0097] Figure 5This study illustrates the spatial distribution of water ice pixels detected within the Nobilli crater. These pixels tend to be located on the left side of the crater and are mostly situated in regions where the highest lunar surface temperature is below 110 K. This low-temperature environment provides the necessary conditions for the long-term preservation of lunar water ice. Furthermore, the water ice detection results are affected by parameter settings. The number of water ice pixels detected within the Nobilli crater decreases rapidly with increasing absorption intensity threshold but increases rapidly with increasing spectral information divergence threshold.

[0098] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for infrared remote sensing detection of water ice in permanently shadowed lunar regions based on spectral information correction, characterized in that, Includes the following steps: Step 1) Combine field-of-view analysis and illumination range to determine the location of scattering sources around pixels in permanent shadow areas; Step 2) Solve for the irradiance at the location of the pixel scattered into the permanent shadow area, including the following steps: Step 2-1) For any scattering source pixel found around a pixel in the permanent shadow region, let the pixel in the permanent shadow region be pixel P and the scattering source pixel be pixel S. Calculate the spectral reflectance from pixel S to pixel P using the Hapke model: in, yes Spectral reflectance of the band Let S be the angle of solar incidence at pixel S. The angle of departure. Phase angle; Angle of incidence The cosine value, For the angle of departure The cosine value; and These are the backscattering function and the phase function, respectively. and A function describing the multiple scattering process; for Single-scattering albedo at pixel S in the band; Step 2-2) Using the solved spectral reflectance, calculate the irradiance scattered from pixel S into pixel P: in, yes Scattered irradiance corresponding to the band yes Spectral irradiance of direct solar radiation in the specified band. for S Visibility of the solar disk at a pixel; Step 3) Calculate the ratio between the satellite sensor observations and the calculated scattered irradiance band by band to obtain the corrected infrared spectral reflectance, including the following steps: Step 3-1) Solve for each surrounding scattering source pixel S1, S2, ..., S n Irradiance scattered into the permanent shadow region of pixel P , ,…, ,in, n This represents the number of scattering source pixels; Step 3-2) Calculate the total scattered irradiance received by the pixels in the permanent shadow region: in, yes The total amount of scattered irradiance received corresponding to the band. yes The surrounding area corresponding to the band k The irradiance of pixel P scattered by a scattering source pixel into the permanent shadow region. Let be the incident angle of the scattered light at pixel P. For the projected solid angle; Step 3-3) Obtain the corrected spectral reflectance by calculating the ratio between irradiance values: in, yes Band-corrected spectral reflectance yes The spectral irradiance recorded in the infrared spectral data corresponding to the band; Step 4) Normalize the corrected spectral reflectance and perform adaptive fitting to obtain the spectral curve; Step 5) Construct a comprehensive criterion for determining the existence of water ice, and determine the location of water ice based on the spectral curve.

2. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 1, characterized in that, Step 1) includes the following steps: Step 1-1) For any pixel within the permanently shadowed area, obtain the surrounding visible area based on the lunar surface topography using field-of-view analysis; Steps 1-2) Extract the shooting time of the infrared spectral data of the location of the permanent shadowed area pixels, and obtain the range of the sun's direct rays at that time based on ephemeris information and lunar surface topography; Steps 1-3) Intersect the visible area around the permanent shadowed pixel with the area directly exposed to the sun to obtain the distribution area and number of the surrounding scattering source pixels.

3. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 1, characterized in that, Step 4) includes the following steps: Step 4-1) Normalize the spectral reflectance of the pixels in the permanent shadow area after correction: in, for Normalized spectral reflectance corresponding to the band for The corrected spectral reflectance corresponding to the band, and These represent the minimum and maximum spectral reflectance within the research band, respectively. Step 4-2) Use the CSAPS method to perform adaptive smoothing fitting on the normalized spectral reflectance to obtain the fitted spectral curve.

4. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 3, characterized in that, The research band range is 1.0-2.5μm.

5. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 1, characterized in that, Step 5) includes the following steps: Step 5-1) For pixels in the permanent shaded area, use spectral continuum analysis to find the center position of the absorption band on the fitted spectral curve; Step 5-2) Absorption feature judgment: Compare the center position of the found absorption spectrum with the known water ice absorption spectrum. If the spectral absorption center is found in the known water ice absorption spectrum and the absorption feature is obvious, that is, the absorption intensity is greater than the preset threshold, then the permanent shadow area pixel is considered to be a potential water ice pixel. Step 5-3) Spectral similarity analysis: For the detected potential water ice pixels, evaluate the degree of similarity between them and the spectrum of pure water ice in the laboratory; Step 5-4) Extract potential water ice pixels that meet the preset conditions in terms of similarity to the spectrum of laboratory pure water ice, and mark them as the finally detected water ice pixels.

6. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 5, characterized in that, In step 5-3), the evaluation indicators for assessing the similarity between the spectrum of the spectrum and that of laboratory pure water ice include spectral angle and spectral information divergence.

7. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 6, characterized in that, The methods for calculating the spectral angle and spectral information divergence are as follows: in, , refers to the spectral reflectance vector of potential water ice pixels in the permanently shaded area; , refers to the spectral reflectance vector of laboratory pure water ice; For the first i Spectral reflectance of potential water ice pixels in a permanently shaded area. For the first i Spectral reflectance of pure water ice in a laboratory n This represents the quantity of spectral reflectance.

8. The infrared remote sensing method for detecting water ice in permanently shadowed lunar regions based on spectral information correction according to claim 7, characterized in that, In step 5-4), when the spectral angle corresponding to the potential water ice pixel is <30º and the spectral information divergence is <8, it is marked as the finally detected water ice pixel.

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