Non-binary Snow Index for Multi-Component Surfaces
A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs) such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separab...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2107.05574 https://arxiv.org/abs/2107.05574 |
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ftdatacite:10.48550/arxiv.2107.05574 2023-05-15T16:29:31+02:00 Non-binary Snow Index for Multi-Component Surfaces Arreola-Esquivel, Mario M. Toxqui-Quitl, Carina Delgadillo-Herrera, Maricela Padilla-Vivanco, Alfonso Ortega-Mendoza, José G. Carbone, Anna 2021 https://dx.doi.org/10.48550/arxiv.2107.05574 https://arxiv.org/abs/2107.05574 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Atmospheric and Oceanic Physics physics.ao-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences article-journal Article ScholarlyArticle Text 2021 ftdatacite https://doi.org/10.48550/arxiv.2107.05574 2022-03-10T13:55:43Z A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs) such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, Greenland and France-Italy regions were examined where snow interacts with highly diversified geographical ecosystem. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites have been used. The NBSI-MS performance was also compared against the well-known NDSI, NDSII-1, S3, and SWI methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieves overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as OA. The precision assessment confirms the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices. : 22 pages, 12 figures Article in Journal/Newspaper Greenland DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
Atmospheric and Oceanic Physics physics.ao-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences |
spellingShingle |
Atmospheric and Oceanic Physics physics.ao-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences Arreola-Esquivel, Mario M. Toxqui-Quitl, Carina Delgadillo-Herrera, Maricela Padilla-Vivanco, Alfonso Ortega-Mendoza, José G. Carbone, Anna Non-binary Snow Index for Multi-Component Surfaces |
topic_facet |
Atmospheric and Oceanic Physics physics.ao-ph Data Analysis, Statistics and Probability physics.data-an FOS Physical sciences |
description |
A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs) such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, Greenland and France-Italy regions were examined where snow interacts with highly diversified geographical ecosystem. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites have been used. The NBSI-MS performance was also compared against the well-known NDSI, NDSII-1, S3, and SWI methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieves overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as OA. The precision assessment confirms the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices. : 22 pages, 12 figures |
format |
Article in Journal/Newspaper |
author |
Arreola-Esquivel, Mario M. Toxqui-Quitl, Carina Delgadillo-Herrera, Maricela Padilla-Vivanco, Alfonso Ortega-Mendoza, José G. Carbone, Anna |
author_facet |
Arreola-Esquivel, Mario M. Toxqui-Quitl, Carina Delgadillo-Herrera, Maricela Padilla-Vivanco, Alfonso Ortega-Mendoza, José G. Carbone, Anna |
author_sort |
Arreola-Esquivel, Mario M. |
title |
Non-binary Snow Index for Multi-Component Surfaces |
title_short |
Non-binary Snow Index for Multi-Component Surfaces |
title_full |
Non-binary Snow Index for Multi-Component Surfaces |
title_fullStr |
Non-binary Snow Index for Multi-Component Surfaces |
title_full_unstemmed |
Non-binary Snow Index for Multi-Component Surfaces |
title_sort |
non-binary snow index for multi-component surfaces |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2107.05574 https://arxiv.org/abs/2107.05574 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2107.05574 |
_version_ |
1766019218770231296 |