Byers Peninsula (62°34’-62°40’S-60°54’61°13’W), 60 km2 in size, is considered one of the
largest ice-free areas in Antarctica. Since 2006, the Spanish Polar Program has taken part in a large
number of environmental studies involving effects of climate changes, limnology and microbiology,
live cycles, but not albedo. Surface albedo is one of the key physical parameters in the surface energy
budget of polar regions. Most of Antarctica is covered by ice sheets; only about 0.44% of the area is
permanently ice-free. However, in maritime Antarctica, the ice-free areas, corresponding to small
islands, peninsulas, and coastal beaches, account for about 3% of the territory. To incorporate the
contribution of these areas into global albedo models, it is necessary to relate the soil properties of
the ice-free areas to the surface albedo response. Also, mapping soil properties and albedo have and
special interest in these ice-free areas. Image classification using machine learning methods trained
with georeferenced soil data could be useful for mapping soil properties and albedo in multispectral
optical satellite images. A shallow neural network implemented using the Keras Python module was
used to define and train models of soil properties using 15 explanatory variables corresponding to
bands and spectral indices of a Sentinel image and a population of 49 soil samples taken from the
top 5 centimeters of the soil profile. The soil samples were analyzed in the laboratory and a spectral
library in the Vis-Nir range (350-2500 nm) was created. The albedo of the samples was integrated
from the ADS spectra. At the same time, a linear regression model of albedo using the soil properties
as explanatory variables was performed. The R2 fit of this new model was about 0.82 and the error of
estimation was 4.1. The model was extended to the entire Byers Peninsula using the ML soil property
models as explanatory variables. The RMSE of the extended models increased up to 8.2. We think
that the main cause of this increase (2.6 points) is related to the error propagation, phenomenon due
to the use of models as explanatory variables. However, another important part of the error increase
could be due, among other reasons, to the use of an image that, although corrected, is not completely
free of clouds.