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A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research
Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for thei...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082855/ https://www.ncbi.nlm.nih.gov/pubmed/37031236 http://dx.doi.org/10.1038/s41597-023-02096-0 |
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author | Montero, David Aybar, César Mahecha, Miguel D. Martinuzzi, Francesco Söchting, Maximilian Wieneke, Sebastian |
author_facet | Montero, David Aybar, César Mahecha, Miguel D. Martinuzzi, Francesco Söchting, Maximilian Wieneke, Sebastian |
author_sort | Montero, David |
collection | PubMed |
description | Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present “Awesome Spectral Indices” (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains. |
format | Online Article Text |
id | pubmed-10082855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100828552023-04-10 A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research Montero, David Aybar, César Mahecha, Miguel D. Martinuzzi, Francesco Söchting, Maximilian Wieneke, Sebastian Sci Data Data Descriptor Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present “Awesome Spectral Indices” (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains. Nature Publishing Group UK 2023-04-08 /pmc/articles/PMC10082855/ /pubmed/37031236 http://dx.doi.org/10.1038/s41597-023-02096-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Montero, David Aybar, César Mahecha, Miguel D. Martinuzzi, Francesco Söchting, Maximilian Wieneke, Sebastian A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title | A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title_full | A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title_fullStr | A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title_full_unstemmed | A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title_short | A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research |
title_sort | standardized catalogue of spectral indices to advance the use of remote sensing in earth system research |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082855/ https://www.ncbi.nlm.nih.gov/pubmed/37031236 http://dx.doi.org/10.1038/s41597-023-02096-0 |
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