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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Rodriguez-Cielos, RicardoAuthor

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April 21, 2024
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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)

Publicated to: Remote Sensing. 16 (7): 1192- - 2024-04-01 16(7), DOI: 10.3390/rs16071192

Authors:

Fernández, SD; Muñiz, R; Peón, J; Rodríguez-Cielos, R; Ruíz, J; Calleja, JF
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Affiliations

Univ Oviedo, Dept Comp Sci, Oviedo 33003, Spain - Author
Univ Oviedo, Dept Geog, Oviedo 33003, Spain - Author
Univ Oviedo, Dept Geol, Oviedo 33003, Spain - Author
Univ Oviedo, Dept Min Exploitat & Prospecting, Mieres 33600, Spain - Author
Univ Oviedo, Dept Phys, Oviedo 33003, Spain - Author
Univ Oviedo, ICTEA Inst Univ Ciencias & Tecnol Aerosp Asturias, Oviedo 33003, Spain - Author
Univ Politecn Madrid, Dept Signals Syst & Radiocommun SSR, Madrid 28040, Spain - Author
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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 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.
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Keywords

CommunitiesDeep learningDissolved organic carbonEartEcosystemLife below waterLimnopolar lakeLinear regressionLivingston islandMaritime antarcticaRecordSatellite imagerSatellite imagerySoilsSouth-shetland islands

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 47/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • WoS: 1
  • Scopus: 1
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 11 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/90017/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 135
    • Downloads: 13
    Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 14 - Life below water, with a probability of 75% according to the mBERT algorithm developed by Aurora University.
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    Awards linked to the item

    No Statement Available
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