Articles | Volume 10, issue 2
https://doi.org/10.5194/tc-10-569-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-569-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Mar 2016
Research article |
| 11 Mar 2016
Monitoring ice break-up on the Mackenzie River using MODIS data
P. Muhammad
CORRESPONDING AUTHOR
Department of Geography and the Interdisciplinary Centre on Climate Change (IC3), University of Waterloo, Waterloo, ON, Canada
Department of Geography and the Interdisciplinary Centre on Climate Change (IC3), University of Waterloo, Waterloo, ON, Canada
K.-K. Kang
Department of Geography and the Interdisciplinary Centre on Climate Change (IC3), University of Waterloo, Waterloo, ON, Canada
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Cited
23 citations as recorded by crossref.
- A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping S. Kraatz et al. https://doi.org/10.3390/rs9030229
- River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark C. Wei et al. https://doi.org/10.1109/TGRS.2025.3604644
- Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands H. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126613
- Mapping and Revealing the River Ice Distribution and Changes in the Three Rivers Source Region From 1990 to 2023 Using Google Earth Engine H. Li et al. https://doi.org/10.1109/JSTARS.2025.3649347
- Long-term retrieval (1998-2023) of colored dissolved organic matter absorption coefficient and dissolved organic carbon in the Mackenzie River-Beaufort Sea region using the Copernicus GlobColour product M. Sánchez-Urrea et al. https://doi.org/10.3389/frsen.2026.1768049
- Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska K. Bennett et al. https://doi.org/10.5194/hess-23-2439-2019
- Analytical model for incipient sediment motion in ice-covered channels: Integrating multi-motion modes of saltation, rolling, and sliding S. Zhao et al. https://doi.org/10.1063/5.0337459
- A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea Y. Choi et al. https://doi.org/10.3390/rs14030793
- Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar H. Zhang et al. https://doi.org/10.3390/rs14112591
- Study on the spatiotemporal distribution characteristics and influencing factors of open water during the freezing period in the Inner Mongolia Section of the Yellow River from 1986 to 2023 B. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102423
- DFF-Net: A Dual-Branch Feature Fusion Network for River Ice Mapping via the Fusion of Microwave and Optical Imagery H. Li et al. https://doi.org/10.1109/JSTARS.2026.3694209
- Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta F. Zhang et al. https://doi.org/10.3390/w9050306
- River ice phenology and thickness from satellite altimetry: potential for ice bridge road operation and climate studies E. Zakharova et al. https://doi.org/10.5194/tc-15-5387-2021
- Spatial-temporal variation of river ice coverage in the Yenisei river from 2002 to 2021 Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.131440
- Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data C. Normandin et al. https://doi.org/10.5194/hess-22-1543-2018
- How Ice Mapping Can Help Manage and Prevent Ice Jams: Remote Sensing Monitoring of the Saint-François River, Québec V. Plante Lévesque et al. https://doi.org/10.1080/07038992.2024.2391972
- River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river X. Zhang et al. https://doi.org/10.1007/s11042-021-11054-0
- Review of River Ice Observation and Data Analysis Technologies I. Zakharov et al. https://doi.org/10.3390/hydrology11080126
- A physically-based modelling framework for operational forecasting of river ice breakup P. Rokaya et al. https://doi.org/10.1016/j.advwatres.2020.103554
- Revealing the river ice phenology on the Tibetan Plateau using Sentinel-2 and Landsat 8 overlapping orbit imagery H. Li et al. https://doi.org/10.1016/j.jhydrol.2023.129285
- Effects of River Geomorphology on River Ice Freeze-up and Break-up Rates Using MODIS Imagery T. Chu & K. Lindenschmidt https://doi.org/10.1080/07038992.2019.1635004
- Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018 H. Li et al. https://doi.org/10.1016/j.rse.2020.111915
- Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario A. Beaton et al. https://doi.org/10.1016/j.rse.2019.02.011
23 citations as recorded by crossref.
- A Comparison of MODIS/VIIRS Cloud Masks over Ice-Bearing River: On Achieving Consistent Cloud Masking and Improved River Ice Mapping S. Kraatz et al. https://doi.org/10.3390/rs9030229
- River Ice Fine-Grained Segmentation: A GF-2 Satellite Image Dataset and Deep Learning Benchmark C. Wei et al. https://doi.org/10.1109/TGRS.2025.3604644
- Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands H. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126613
- Mapping and Revealing the River Ice Distribution and Changes in the Three Rivers Source Region From 1990 to 2023 Using Google Earth Engine H. Li et al. https://doi.org/10.1109/JSTARS.2025.3649347
- Long-term retrieval (1998-2023) of colored dissolved organic matter absorption coefficient and dissolved organic carbon in the Mackenzie River-Beaufort Sea region using the Copernicus GlobColour product M. Sánchez-Urrea et al. https://doi.org/10.3389/frsen.2026.1768049
- Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska K. Bennett et al. https://doi.org/10.5194/hess-23-2439-2019
- Analytical model for incipient sediment motion in ice-covered channels: Integrating multi-motion modes of saltation, rolling, and sliding S. Zhao et al. https://doi.org/10.1063/5.0337459
- A Maritime Cloud-Detection Method Using Visible and Near-Infrared Bands over the Yellow Sea and Bohai Sea Y. Choi et al. https://doi.org/10.3390/rs14030793
- Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar H. Zhang et al. https://doi.org/10.3390/rs14112591
- Study on the spatiotemporal distribution characteristics and influencing factors of open water during the freezing period in the Inner Mongolia Section of the Yellow River from 1986 to 2023 B. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102423
- DFF-Net: A Dual-Branch Feature Fusion Network for River Ice Mapping via the Fusion of Microwave and Optical Imagery H. Li et al. https://doi.org/10.1109/JSTARS.2026.3694209
- Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta F. Zhang et al. https://doi.org/10.3390/w9050306
- River ice phenology and thickness from satellite altimetry: potential for ice bridge road operation and climate studies E. Zakharova et al. https://doi.org/10.5194/tc-15-5387-2021
- Spatial-temporal variation of river ice coverage in the Yenisei river from 2002 to 2021 Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.131440
- Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data C. Normandin et al. https://doi.org/10.5194/hess-22-1543-2018
- How Ice Mapping Can Help Manage and Prevent Ice Jams: Remote Sensing Monitoring of the Saint-François River, Québec V. Plante Lévesque et al. https://doi.org/10.1080/07038992.2024.2391972
- River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river X. Zhang et al. https://doi.org/10.1007/s11042-021-11054-0
- Review of River Ice Observation and Data Analysis Technologies I. Zakharov et al. https://doi.org/10.3390/hydrology11080126
- A physically-based modelling framework for operational forecasting of river ice breakup P. Rokaya et al. https://doi.org/10.1016/j.advwatres.2020.103554
- Revealing the river ice phenology on the Tibetan Plateau using Sentinel-2 and Landsat 8 overlapping orbit imagery H. Li et al. https://doi.org/10.1016/j.jhydrol.2023.129285
- Effects of River Geomorphology on River Ice Freeze-up and Break-up Rates Using MODIS Imagery T. Chu & K. Lindenschmidt https://doi.org/10.1080/07038992.2019.1635004
- Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018 H. Li et al. https://doi.org/10.1016/j.rse.2020.111915
- Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario A. Beaton et al. https://doi.org/10.1016/j.rse.2019.02.011
Saved (final revised paper)
Latest update: 10 Sep 2026
Short summary
This study involves the analysis of MODIS Level 3500 m snow products, complemented with 250 m Level 1B data, to monitor ice cover during the break-up period on the Mackenzie River, Canada. Results from the analysis of data for 13 ice seasons (2001–2013) show that ice-off begins between days of year (DOYs) 115 and 125 and ends between DOYs 145 and 155, resulting in average melt durations of about 30–40 days; we conclude that MODIS can monitor ice break-up.
This study involves the analysis of MODIS Level 3500 m snow products, complemented with 250 m...
