An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales
Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-sc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1231 |
| _version_ | 1827752323151036416 |
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| author | Lars Keuris Markus Hetzenecker Thomas Nagler Nico Mölg Gabriele Schwaizer |
| author_facet | Lars Keuris Markus Hetzenecker Thomas Nagler Nico Mölg Gabriele Schwaizer |
| author_sort | Lars Keuris |
| collection | DOAJ |
| description | Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions. |
| first_indexed | 2024-03-11T07:12:28Z |
| format | Article |
| id | doaj.art-a50be3a9f9424363b71d35c05a763933 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:28Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a50be3a9f9424363b71d35c05a7639332023-11-17T08:30:13ZengMDPI AGRemote Sensing2072-42922023-02-01155123110.3390/rs15051231An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global ScalesLars Keuris0Markus Hetzenecker1Thomas Nagler2Nico Mölg3Gabriele Schwaizer4ENVEO IT GmbH, Fürstenweg 176, 6020 Innsbruck, AustriaENVEO IT GmbH, Fürstenweg 176, 6020 Innsbruck, AustriaENVEO IT GmbH, Fürstenweg 176, 6020 Innsbruck, AustriaENVEO IT GmbH, Fürstenweg 176, 6020 Innsbruck, AustriaENVEO IT GmbH, Fürstenweg 176, 6020 Innsbruck, AustriaSnow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.https://www.mdpi.com/2072-4292/15/5/1231snow covermulti-spectralunmixingSentinelLandsaterror estimation |
| spellingShingle | Lars Keuris Markus Hetzenecker Thomas Nagler Nico Mölg Gabriele Schwaizer An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales Remote Sensing snow cover multi-spectral unmixing Sentinel Landsat error estimation |
| title | An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales |
| title_full | An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales |
| title_fullStr | An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales |
| title_full_unstemmed | An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales |
| title_short | An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales |
| title_sort | adaptive method for the estimation of snow covered fraction with error propagation for applications from local to global scales |
| topic | snow cover multi-spectral unmixing Sentinel Landsat error estimation |
| url | https://www.mdpi.com/2072-4292/15/5/1231 |
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