Abstract
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.
Highlights
We showed in previous work that STC(Aqua) vs. NASA(Aqua) outside of snow/ice cover provided similar effective revisit times (4.6 vs. 4.0 days), but performed substantially better during snow/ice cover (3.8 vs. 7.1 days), with no non-detections or false detections with respect to hydrometric station data
We find that NASA(VIIRS) detects substantially fewer clouds than NASA(MODIS)
The main goal of this work was to elucidate on cloud mask performance and suitability over ice-bearing rivers and on improving river ice observations
Summary
Radiometer Suite (VIIRS) instruments, owing to large observing swaths and polar orbits, allow for daily or better global coverage at up 250 and 375 m spatial resolutions, respectively. Frequent observations are desirable to monitor ice-bearing rivers, since ice jams may cause flooding within hours 2017, 9, 229 important to observe wide swaths, since hydrometric stations have greatly declined in number since the 1980s [1,2], and may by themselves not provide adequate coverage for flood warning or detection. With respect to ice jams, it is important to know the river condition over an extended area up- and downstream of the jam. In light of flood detection being a vital and current issue, the Joint Polar
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