Javier F. Calleja

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles.

Byers Peninsula is considered one of the largest ice-free areas in maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving the effects of climate change on biological life cycles, limnology, and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical, and morphological characteristics that are strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. Mapping and monitoring those areas that are highly occupied by various species could be very useful to create models prepared from satellite images of the edaphic properties. In this approach, deep learning and linear regression models of the soil properties and spectral indexes, which were considered as explicative variables, were used. We trained the models on soil properties closely related to biological activity such as dissolved organic carbon (DOC) and the iron fraction associated with the organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe, and pH by training the linear regression and deep learning models on Sentinel-2 and WorldView-2 images. The most robust models, the pH model built with the deep learning approach on Sentinel images (MAE of 0.51, RMSE of 0.70, and R2 with a residual of −0.49), the DOC model built with linear regression on Sentinel images (MAE of 189.39, RMSE of 342.23, and R2 with a residual of 0.0), and the organic Fe model built with deep learning (MAE of 116.20, RMSE of 209.93, and R2 of −0.05), were used to track possible areas with ornithogenic soils, as well as areas of Byers Peninsula that could be supporting the highest biological development.