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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/14/2777 |
| _version_ | 1797526092158861312 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T09:24:17Z |
| format | Article |
| id | doaj.art-041a6f4e89744bf09523c2dfe0eb45bd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:24:17Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-041a6f4e89744bf09523c2dfe0eb45bd2023-11-22T04:52:15ZengMDPI AGRemote Sensing2072-42922021-07-011314277710.3390/rs13142777Non-Binary Snow Index for Multi-Component SurfacesMario Arreola-Esquivel0Carina Toxqui-Quitl1Maricela Delgadillo-Herrera2Alfonso Padilla-Vivanco3Gabriel Ortega-Mendoza4Anna Carbone5Computer Vision Laboratory, Universidad Politécnica de Tulancingo, Hidalgo 43625, MexicoComputer Vision Laboratory, Universidad Politécnica de Tulancingo, Hidalgo 43625, MexicoComputer Vision Laboratory, Universidad Politécnica de Tulancingo, Hidalgo 43625, MexicoComputer Vision Laboratory, Universidad Politécnica de Tulancingo, Hidalgo 43625, MexicoComputer Vision Laboratory, Universidad Politécnica de Tulancingo, Hidalgo 43625, MexicoDepartment of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, ItalyA 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.https://www.mdpi.com/2072-4292/13/14/2777NDSINDSII-1S3SWINBSI-MSLandsat 5 TM |
| spellingShingle | 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 Remote Sensing NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM |
| title | 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_short | Non-Binary Snow Index for Multi-Component Surfaces |
| title_sort | non binary snow index for multi component surfaces |
| topic | NDSI NDSII-1 S3 SWI NBSI-MS Landsat 5 TM |
| url | https://www.mdpi.com/2072-4292/13/14/2777 |
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