A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping
The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/9/3/229 |
| _version_ | 1818958923042914304 |
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| author | Simon Kraatz Reza Khanbilvardi Peter Romanov |
| author_facet | Simon Kraatz Reza Khanbilvardi Peter Romanov |
| author_sort | Simon Kraatz |
| collection | DOAJ |
| description | The capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and owing to differences in detector performance, it is quite possible that identical algorithms applied to even nearly identical instruments may produce substantially different cloud masks. Besides detector performance, cloud identification can be biased due to local (e.g., land cover), viewing geometry, and transient conditions (snow and ice). Snow/cloud confusions and large view angles can result in substantial overestimates of clouds and ice. This impacts algorithms, such as CRIOS, since false cloud cover precludes the determination of whether an otherwise reasonably cloud free grid consists of water or ice. Especially for applications aiming to frequently classify or monitor a location it is important to evaluate cloud masking, including false cloud detections. We present an assessment of three cloud masks via the parameter of effective revisit time. A 100 km stretch of up to 1.6 km wide river was examined with daily data sampled at 500 m resolution, examined over 317 days during winter. Results show that there are substantial differences between each of the cloud mask products, especially while the river bears ice. A contrast-based cloud screening approach was found to provide improved and consistent cloud and ice identification within the reach (95%–99% correlations, and 3%–7% mean absolute differences) between the independently observing platforms. River ice was also detected accurately (proportion correct 95%–100%) and more frequently. Owing to cross-platform compositing, it is possible to obtain an effective revisit time of 2.8 days and further error reductions. |
| first_indexed | 2024-12-20T11:33:27Z |
| format | Article |
| id | doaj.art-1bec96a471ff418a8e2ba952354b3d53 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:33:27Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1bec96a471ff418a8e2ba952354b3d532022-12-21T19:42:11ZengMDPI AGRemote Sensing2072-42922017-03-019322910.3390/rs9030229rs9030229A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice MappingSimon Kraatz0Reza Khanbilvardi1Peter Romanov2NOAA-CREST Institute, The City University of New York, New York, NY 10031, USANOAA-CREST Institute, The City University of New York, New York, NY 10031, USANOAA-CREST Institute, The City University of New York, New York, NY 10031, USAThe capability of frequently and accurately monitoring ice on rivers is important, since it may be possible to timely identify ice accumulations corresponding to ice jams. Ice jams are dam-like structures formed from arrested ice floes, and may cause rapid flooding. To inform on this potential hazard, the CREST River Ice Observing System (CRIOS) produces ice cover maps based on MODIS and VIIRS overpass data at several locations, including the Susquehanna River. CRIOS uses the respective platform’s automatically produced cloud masks to discriminate ice/snow covered grid cells from clouds. However, since cloud masks are produced using each instrument’s data, and owing to differences in detector performance, it is quite possible that identical algorithms applied to even nearly identical instruments may produce substantially different cloud masks. Besides detector performance, cloud identification can be biased due to local (e.g., land cover), viewing geometry, and transient conditions (snow and ice). Snow/cloud confusions and large view angles can result in substantial overestimates of clouds and ice. This impacts algorithms, such as CRIOS, since false cloud cover precludes the determination of whether an otherwise reasonably cloud free grid consists of water or ice. Especially for applications aiming to frequently classify or monitor a location it is important to evaluate cloud masking, including false cloud detections. We present an assessment of three cloud masks via the parameter of effective revisit time. A 100 km stretch of up to 1.6 km wide river was examined with daily data sampled at 500 m resolution, examined over 317 days during winter. Results show that there are substantial differences between each of the cloud mask products, especially while the river bears ice. A contrast-based cloud screening approach was found to provide improved and consistent cloud and ice identification within the reach (95%–99% correlations, and 3%–7% mean absolute differences) between the independently observing platforms. River ice was also detected accurately (proportion correct 95%–100%) and more frequently. Owing to cross-platform compositing, it is possible to obtain an effective revisit time of 2.8 days and further error reductions.http://www.mdpi.com/2072-4292/9/3/229cloud maskriver iceMODISVIIRSSusquehannasnow |
| spellingShingle | Simon Kraatz Reza Khanbilvardi Peter Romanov A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping Remote Sensing cloud mask river ice MODIS VIIRS Susquehanna snow |
| title | A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping |
| title_full | A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping |
| title_fullStr | A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping |
| title_full_unstemmed | A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping |
| title_short | A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping |
| title_sort | comparison of modis viirs cloud masks over ice bearing river on achieving consistent cloud masking and improved river ice mapping |
| topic | cloud mask river ice MODIS VIIRS Susquehanna snow |
| url | http://www.mdpi.com/2072-4292/9/3/229 |
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