DOI: 10.57760/sciencedb.34802
title: A High-Resolution Sentinel-2 Dataset of Antarctic Coastal Snow Algal Dynamics (2018–2025) Generated Using Cloud-Scale Parallel Processing
title: A High-Resolution Sentinel-2 Dataset of Antarctic Coastal Snow Algal Dynamics (2018–2025) Generated Using Cloud-Scale Parallel Processing

description: Antarctica plays an important role in global environmental research, and its extensive coastal snow environments provide a unique setting for observing biological processes in the cryosphere. Satellite remote sensing offers an effective means to monitor these remote regions at large spatial scales, and multispectral optical imagery is particularly suitable for detecting photosynthetic activity on snow surfaces. The Sentinel-2A and Sentinel-2B satellites provide high spatial resolution, frequent revisit coverage, and multispectral observations well suited for this purpose. This dataset consists of high-resolution (10 m) Antarctic coastal snow algal bloom products derived from Sentinel-2 Level-2A imagery acquired during the austral summer seasons (October–March) from 2018 to 2025. All available cloud-screened scenes (25061 images) within a 100 km coastal buffer along the western Antarctic margin were processed using cloud-based workflows in Google Earth Engine, including cloud and shadow masking, seasonal median compositing, IB4 calculation, conversion to algal cell concentration estimates, and false-positive filtering. Large-scale tiled and parallel processing was employed to manage the multi-year archive and ensure consistent trend product generation. The resulting dataset provides standardized seasonal raster layers of the IB4 spectral index, estimated snow algal cell concentration, and pixel-level temporal trend products derived using a modified Mann–Kendall test, suitable for Antarctic environmental monitoring and remote sensing research.
description: Antarctica plays an important role in global environmental research, and its extensive coastal snow environments provide a unique setting for observing biological processes in the cryosphere. Satellite remote sensing offers an effective means to monitor these remote regions at large spatial scales, and multispectral optical imagery is particularly suitable for detecting photosynthetic activity on snow surfaces. The Sentinel-2A and Sentinel-2B satellites provide high spatial resolution, frequent revisit coverage, and multispectral observations well suited for this purpose. This dataset consists of high-resolution (10 m) Antarctic coastal snow algal bloom products derived from Sentinel-2 Level-2A imagery acquired during the austral summer seasons (October–March) from 2018 to 2025. All available cloud-screened scenes (25061 images) within a 100 km coastal buffer along the western Antarctic margin were processed using cloud-based workflows in Google Earth Engine, including cloud and shadow masking, seasonal median compositing, IB4 calculation, conversion to algal cell concentration estimates, and false-positive filtering. Large-scale tiled and parallel processing was employed to manage the multi-year archive and ensure consistent trend product generation. The resulting dataset provides standardized seasonal raster layers of the IB4 spectral index, estimated snow algal cell concentration, and pixel-level temporal trend products derived using a modified Mann–Kendall test, suitable for Antarctic environmental monitoring and remote sensing research.