{"title":"Identification of H2O ice at lunar south polar region based on deep learning","authors":"Tianwei Wang, Jian Chen, Changqing Liu, Ziyuan Wang, Haijun Cao, Zongcheng Ling","doi":"10.1016/j.icarus.2025.116682","DOIUrl":null,"url":null,"abstract":"<div><div>The presence of frozen volatiles (especially H<sub>2</sub>O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H<sub>2</sub>O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H<sub>2</sub>O-ice identification task. Here we present H<sub>2</sub>O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H<sub>2</sub>O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H<sub>2</sub>O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H<sub>2</sub>O ice. The spectral characteristics of H<sub>2</sub>O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H<sub>2</sub>O ice are also discussed. H<sub>2</sub>O ice in the lunar south polar region tends to exist in the form of smaller particles (∼70 μm in size), while the weak/absent 2-μm absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.</div></div>","PeriodicalId":13199,"journal":{"name":"Icarus","volume":"441 ","pages":"Article 116682"},"PeriodicalIF":2.5000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Icarus","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019103525002295","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
The presence of frozen volatiles (especially H2O ice) has been proposed in the permanently shadowed regions (PSRs) near the poles of the Moon, based on various remote measurements including the visible and near-infrared (VNIR) spectroscopy. Compared with the middle- and low-latitude areas, the VNIR spectral signals in the PSRs are noisy due to poor solar illumination. Coupled with the lunar regolith coverage and mixing effects, the available VNIR spectral characteristics for the identification of H2O ice in the PSRs are limited. Deep learning models, as emerging techniques in lunar exploration, are able to learn spectral features and patterns, and discover complex spectral patterns and nonlinear relationships from large datasets, enabling them applicable on lunar hyperspectral remote sensing data and H2O-ice identification task. Here we present H2O ice identification results by a deep learning-based model named one-dimensional convolutional autoencoder. During the model application, there are intrinsic differences between the remote sensing spectra obtained by the orbital spectrometers and the laboratory spectra acquired by state-of-the-art instruments. To address the challenges of limited training data and the difficulty of matching laboratory and remote sensing spectra, we introduce self-supervised learning method to achieve pixel-level identification and mapping of H2O ice in the lunar south polar region. Our model is applied to the level 2 reflectance data of Moon Mineralogy Mapper. The spectra of the identified H2O ice-bearing pixels were extracted to perform dual validation using spectral angle mapping and peak clustering methods, further confirming the identification of most pixels containing H2O ice. The spectral characteristics of H2O ice in the lunar south polar region related to the crystal structure, grain size, and mixing effect of H2O ice are also discussed. H2O ice in the lunar south polar region tends to exist in the form of smaller particles (∼70 μm in size), while the weak/absent 2-μm absorption indicate the existence of unusually large particles. Crystalline ice is the main phase responsible for the identified spectra of ice-bearing surface however the possibility of amorphous H2O ice beneath optically sensed depth cannot be ruled out.
期刊介绍:
Icarus is devoted to the publication of original contributions in the field of Solar System studies. Manuscripts reporting the results of new research - observational, experimental, or theoretical - concerning the astronomy, geology, meteorology, physics, chemistry, biology, and other scientific aspects of our Solar System or extrasolar systems are welcome. The journal generally does not publish papers devoted exclusively to the Sun, the Earth, celestial mechanics, meteoritics, or astrophysics. Icarus does not publish papers that provide "improved" versions of Bode''s law, or other numerical relations, without a sound physical basis. Icarus does not publish meeting announcements or general notices. Reviews, historical papers, and manuscripts describing spacecraft instrumentation may be considered, but only with prior approval of the editor. An entire issue of the journal is occasionally devoted to a single subject, usually arising from a conference on the same topic. The language of publication is English. American or British usage is accepted, but not a mixture of these.