Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland

Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various...

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Bibliographic Details
Main Authors: Liu, Yijing, Wang, Peiyan, Elberling, Bo, Westergaard-Nielsen, Andreas
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2023
Subjects:
Arctic ecosystems
Climate change
land surface phenology
snow cover
vegetation index
Greenland
Tundra
Online Access:https://doi.org/10.5061/dryad.jsxksn0hp
Description
Summary:Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVI max ) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively. Funding provided by: Danish National Research Foundation Crossref Funder Registry ID: https://ror.org/00znyv691 Award Number: CENPERM DNRF100 Funding provided by: China Scholarship Council Crossref Funder Registry ID: https://ror.org/04atp4p48 Award Number: 202006180016 Funding provided by: National Natural Science Foundation of China Crossref Funder Registry ID: https://ror.org/01h0zpd94 Award Number: 32201360 Funding provided by: The Velux Foundations Crossref Funder Registry ID: https://ror.org/007ww2d15 Award Number: 42069 To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine ( https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 ). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here ...

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