Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mapping Dissolved Organic Carbon (DOC) and Organic Iron by Comparing Deep Learning and Linear Regression Techniques Using Sentinel 2 and WorldView 2 Imagery (Byers Peninsula, Marine Antarctica)

Susana del Carmen Fernández
* ORCID logo ,
Rubén Muñiz
ORCID logo ,
Juanjo Peón
ORCID logo ,
Ricardo Rodríguez-Cielos
ORCID logo ,
Jesús Ruíz
ORCID logo ,
Javier F. Calleja
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Version 1 : Received: 25 February 2024 / Approved: 26 February 2024 / Online: 27 February 2024 (13:28:44 CET)

A peer-reviewed article of this Preprint also exists.

Fernández, S.C.; Muñiz, R.; Peón, J.; Rodríguez-Cielos, R.; Ruíz, J.; Calleja, J.F. Mapping Dissolved Organic Carbon and Organic Iron by Comparing Deep Learning and Linear Regression Techniques Using Sentinel-2 and WorldView-2 Imagery (Byers Peninsula, Maritime Antarctica). Remote Sens. 2024, 16, 1192. Fernández, S.C.; Muñiz, R.; Peón, J.; Rodríguez-Cielos, R.; Ruíz, J.; Calleja, J.F. Mapping Dissolved Organic Carbon and Organic Iron by Comparing Deep Learning and Linear Regression Techniques Using Sentinel-2 and WorldView-2 Imagery (Byers Peninsula, Maritime Antarctica). Remote Sens. 2024, 16, 1192.

Abstract

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 effects of climate changes in lives cycles, limnology and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical and morphological characteristics 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. To mapping and monitoring those areas with high biological occupation, could be very useful to have models of edaphic properties prepared from satellite images. In this approach, Deep Learning and Linear Regression models of soil properties and spectral indexes as explicative variables were performed. We training models of soil properties closely related to biological activity such as Dissolved Organic Carbon (DOC) and the iron fraction associated with organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe and pH by training models of Linear Regression and Deep Learning over Sentinel2 and WorldView2 images. The most robust models were used to track possible areas with ornithogenic soils as well as areas of the Byers Peninsula that could support the highest biological development.

Keywords

maritime antarctica; dissolved organic carbon; organic iron; soil mapping; linear regression; deep learning; Sentinel2 images; WordView 2 imagery

Subject

Environmental and Earth Sciences, Ecology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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