Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach
This paper utilizes supervised machine learning approach to infer presence of water features in Moderate Resolution Imaging Spectroradiometer imagery of Greenland ice sheet in the area UPE_U PROMICE automatic weather station (AWS). Reference dataset consisted of water feature mask was extracted from...
Main Author: | |
---|---|
Format: | Dataset |
Language: | unknown |
Published: |
GEUS Dataverse
2020
|
Subjects: | |
Online Access: | https://dx.doi.org/10.22008/fk2/ywc9xa https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/YWC9XA |
id |
ftdatacite:10.22008/fk2/ywc9xa |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.22008/fk2/ywc9xa 2023-05-15T16:27:40+02:00 Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach Kacperski, Kamil 2020 https://dx.doi.org/10.22008/fk2/ywc9xa https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/YWC9XA unknown GEUS Dataverse https://dx.doi.org/10.22008/fk2/ywc9xa/7iry7i dataset Dataset 2020 ftdatacite https://doi.org/10.22008/fk2/ywc9xa https://doi.org/10.22008/fk2/ywc9xa/7iry7i 2021-11-05T12:55:41Z This paper utilizes supervised machine learning approach to infer presence of water features in Moderate Resolution Imaging Spectroradiometer imagery of Greenland ice sheet in the area UPE_U PROMICE automatic weather station (AWS). Reference dataset consisted of water feature mask was extracted from high resolution LANDSAT 8 images and co-located with MODIS imagery to fit the Gradient Boosting Decision Tree model. Despite a highly imbalanced dataset and difficult task, model obtained recall of 28.7% and precision of 40.1% for the water feature class and overall F1 Score of 96.5%. Then, the model was used to produce water masks over the period of time from 2000 to 2012. Obtained results showed a positive correlation with the average air temperature in-situ measurements and moderate consistency along entire period with other research studies. The study shows promising results for the use of machine learning for water feature extraction and suggests using different approaches to enhance results. The code accompanying this project is available in the author’s Github repository. Dataset Greenland Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Greenland |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
description |
This paper utilizes supervised machine learning approach to infer presence of water features in Moderate Resolution Imaging Spectroradiometer imagery of Greenland ice sheet in the area UPE_U PROMICE automatic weather station (AWS). Reference dataset consisted of water feature mask was extracted from high resolution LANDSAT 8 images and co-located with MODIS imagery to fit the Gradient Boosting Decision Tree model. Despite a highly imbalanced dataset and difficult task, model obtained recall of 28.7% and precision of 40.1% for the water feature class and overall F1 Score of 96.5%. Then, the model was used to produce water masks over the period of time from 2000 to 2012. Obtained results showed a positive correlation with the average air temperature in-situ measurements and moderate consistency along entire period with other research studies. The study shows promising results for the use of machine learning for water feature extraction and suggests using different approaches to enhance results. The code accompanying this project is available in the author’s Github repository. |
format |
Dataset |
author |
Kacperski, Kamil |
spellingShingle |
Kacperski, Kamil Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
author_facet |
Kacperski, Kamil |
author_sort |
Kacperski, Kamil |
title |
Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
title_short |
Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
title_full |
Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
title_fullStr |
Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
title_full_unstemmed |
Mapping the hydrological system of the Greenland ice sheet in coarse satellite products with a machine learning approach |
title_sort |
mapping the hydrological system of the greenland ice sheet in coarse satellite products with a machine learning approach |
publisher |
GEUS Dataverse |
publishDate |
2020 |
url |
https://dx.doi.org/10.22008/fk2/ywc9xa https://dataverse01.geus.dk/citation?persistentId=doi:10.22008/FK2/YWC9XA |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_relation |
https://dx.doi.org/10.22008/fk2/ywc9xa/7iry7i |
op_doi |
https://doi.org/10.22008/fk2/ywc9xa https://doi.org/10.22008/fk2/ywc9xa/7iry7i |
_version_ |
1766017123921952768 |