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 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., 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 ..., , # 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 ...