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Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass
Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digita...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455071/ https://www.ncbi.nlm.nih.gov/pubmed/32939222 http://dx.doi.org/10.1080/17538947.2019.1572799 |
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author | Sudmanns, Martin Tiede, Dirk Augustin, Hannah Lang, Stefan |
author_facet | Sudmanns, Martin Tiede, Dirk Augustin, Hannah Lang, Stefan |
author_sort | Sudmanns, Martin |
collection | PubMed |
description | Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses. |
format | Online Article Text |
id | pubmed-7455071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-74550712020-09-14 Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass Sudmanns, Martin Tiede, Dirk Augustin, Hannah Lang, Stefan Int J Digit Earth Articles Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses. Taylor & Francis 2019-02-05 /pmc/articles/PMC7455071/ /pubmed/32939222 http://dx.doi.org/10.1080/17538947.2019.1572799 Text en © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Sudmanns, Martin Tiede, Dirk Augustin, Hannah Lang, Stefan Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title_full | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title_fullStr | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title_full_unstemmed | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title_short | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass |
title_sort | assessing global sentinel-2 coverage dynamics and data availability for operational earth observation (eo) applications using the eo-compass |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455071/ https://www.ncbi.nlm.nih.gov/pubmed/32939222 http://dx.doi.org/10.1080/17538947.2019.1572799 |
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