DOI: 10.5061/dryad.jsxksn0hp
title: Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland

description: 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 (NDVImax)
 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.
description: 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 appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVI<sub>max</sub> day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVI<sub>max</sub> day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero.
description: # Data from: Drivers of contemporary and future changes in Arctic seasonal
 transition dates for a tundra site in coastal Greenland
 The dataset includes all
 original images used in this study to extract seasonal transition dates
 and corresponding results. ## Description of the data and file structure
 Datasets included: (1) The spatial distribution of NDVI values for this
 study region (168 rows and 166 columns). Each file is named in the form of
 '' year-month-day''. For example, a file named
 "2016-05-02'' represents the data for 2nd, May of 2016. The
 normal NDVI values in each file range from -1 to 1, and NaN represents no
 valid value. The folder named 'unique_date_NDVI' refers to the
 spatial distribution of NDVI for all available dates, directly acquired
 from satellite images. The folder named
 'unique_date_NDVI_rm_outlier' refers to the spatial distribution
 of NDVI after quality correction for each date using the described method.
 (2) The extracted phenology indicators for each pixel in this study
 region. Five tables named 'Phe_pixel_XXXX.xlsx' include the
 extracted seasonal transition dates during 2016–2020, pixel by pixel.
 There are 9 columns in each table, they are row number and column number
 (used to describe the specific location of pixel), year, start of spring,
 middle of spring, end of spring, start of fall, middle of fall, and end of
 fall. ## Sharing/Access information All functions regarding the extraction
 of seasonal transition dates can be found here: *
 All parameters and
 associated functions regarding the SnowModel can be found here: *
 All original meteorological data
 in this study is from: *