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A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites
Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurem...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329431/ https://www.ncbi.nlm.nih.gov/pubmed/35896546 http://dx.doi.org/10.1038/s41597-022-01570-5 |
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author | Moon, Minkyu Richardson, Andrew D. Milliman, Thomas Friedl, Mark A. |
author_facet | Moon, Minkyu Richardson, Andrew D. Milliman, Thomas Friedl, Mark A. |
author_sort | Moon, Minkyu |
collection | PubMed |
description | Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products. |
format | Online Article Text |
id | pubmed-9329431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93294312022-07-29 A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites Moon, Minkyu Richardson, Andrew D. Milliman, Thomas Friedl, Mark A. Sci Data Data Descriptor Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products. Nature Publishing Group UK 2022-07-27 /pmc/articles/PMC9329431/ /pubmed/35896546 http://dx.doi.org/10.1038/s41597-022-01570-5 Text en © The Author(s) 2022 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 Moon, Minkyu Richardson, Andrew D. Milliman, Thomas Friedl, Mark A. A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title | A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title_full | A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title_fullStr | A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title_full_unstemmed | A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title_short | A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites |
title_sort | high spatial resolution land surface phenology dataset for ameriflux and neon sites |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329431/ https://www.ncbi.nlm.nih.gov/pubmed/35896546 http://dx.doi.org/10.1038/s41597-022-01570-5 |
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