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 separa...
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ftmdpi:oai:mdpi.com:/2072-4292/13/14/2777/ 2023-08-20T04:06:56+02:00 Non-Binary Snow Index for Multi-Component Surfaces Mario Arreola-Esquivel Carina Toxqui-Quitl Maricela Delgadillo-Herrera Alfonso Padilla-Vivanco Gabriel Ortega-Mendoza Anna Carbone agris 2021-07-14 application/pdf https://doi.org/10.3390/rs13142777 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs13142777 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 14; Pages: 2777 NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM Landsat 8 OLI Sentinel-2A Text 2021 ftmdpi https://doi.org/10.3390/rs13142777 2023-08-01T02:11:29Z 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, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices. Text Greenland MDPI Open Access Publishing Greenland Remote Sensing 13 14 2777 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM Landsat 8 OLI Sentinel-2A |
spellingShingle |
NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM Landsat 8 OLI Sentinel-2A Mario Arreola-Esquivel Carina Toxqui-Quitl Maricela Delgadillo-Herrera Alfonso Padilla-Vivanco Gabriel Ortega-Mendoza Anna Carbone Non-Binary Snow Index for Multi-Component Surfaces |
topic_facet |
NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM Landsat 8 OLI Sentinel-2A |
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, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices. |
format |
Text |
author |
Mario Arreola-Esquivel Carina Toxqui-Quitl Maricela Delgadillo-Herrera Alfonso Padilla-Vivanco Gabriel Ortega-Mendoza Anna Carbone |
author_facet |
Mario Arreola-Esquivel Carina Toxqui-Quitl Maricela Delgadillo-Herrera Alfonso Padilla-Vivanco Gabriel Ortega-Mendoza Anna Carbone |
author_sort |
Mario Arreola-Esquivel |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13142777 |
op_coverage |
agris |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Remote Sensing; Volume 13; Issue 14; Pages: 2777 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs13142777 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13142777 |
container_title |
Remote Sensing |
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13 |
container_issue |
14 |
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2777 |
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1774718310035750912 |