A High-Resolution Record of Surface Melt on Antarctic Ice Shelves using Multi-Source Remote Sensing Data and Deep Learning

Datacite citation style:
de Roda Husman, Sophie; Stef Lhermitte; Bolibar, Jordi; Hu, Zhongyang; Shukla, Shashwat et. al. (2023): A High-Resolution Record of Surface Melt on Antarctic Ice Shelves using Multi-Source Remote Sensing Data and Deep Learning. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/8a8934ef-9407-406f-8bfb-573eb182ec54.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
choose version: version 2 - 2024-07-10 (latest)
version 1 - 2023-07-05
Delft University of Technology logo
usage stats
426
views
450
downloads
geolocation
Antarctic Ice Sheet
lat (N): -60 to -90
lon (E): -180 to 180
time coverage
2016-2021
licence
cc-0.png logo CC0

The dataset provided in this repository corresponds to the original data used in the publication by De Roda Husman et al. (2023) titled "A High-Resolution Record of Surface Melt on Antarctic Ice Shelves using Multi-Source Remote Sensing Data and Deep Learning" (DOI to be announced!). 


The dataset, named UMelt, contains a comprehensive surface melt record for all Antarctic ice shelves. It offers a high spatial resolution of 500 meters and a high temporal resolution of 12 hours, covering the period from 2016 to 2021. Our methodology relies on the utilization of a deep learning model known as U-Net, which integrates microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). 


The data is available for download in two formats: 

1. "Timeseries": This format provides the data at a twice-daily resolution, allowing for detailed analysis over time.

2. "MeltFraction": This format offers a yearly, summed product, providing a consolidated representation of the melt fraction.


Feel free to access and explore the dataset to gain valuable insights into surface melt dynamics on Antarctic ice shelves.

history
  • 2023-07-05 first online, published, posted
publisher
4TU.ResearchData
format
GeoTIFF
funding
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek
organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience & Remote Sensing

DATA

files (1)