Satellite mapping of land surface freeze-thaw and river break-up events at local scales over Alaska
Jinyang Du, University of Montana, jinyang.du@ntsg.umt.edu (Presenter)
John S Kimball, University of Montana, john.kimball@mso.umt.edu
Mahta Moghaddam, University of Southern California, mahta@usc.edu
Peter Kirchner, National Park Service, peter_kirchner@nps.gov
Thomas A. Douglas, US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory, tom.douglas@erdc.dren.mil
Accelerated warming in the northern high latitudes is promoting earlier seasonal thaw and springtime river ice breakups. However, complex freeze-thaw (FT) distribution patterns at local scales (1-100m) driven by variations in terrain and microclimate, snow and soil conditions, vegetation cover, and disturbance are generally not well-captured by available satellite sensors. The influence of regional climate trends in northern high-latitude basins on the connections between upland vegetation and soil conditions, and the spring flood pulse to major rivers have strong implications for seasonal flood cycles, wildlife habitats, water quality, and human welfare. However, these processes and their linkages are poorly quantified, partially due to constraints imposed by the coarse resolution of most current and planned global satellite missions that target surface water monitoring.
To improve understanding and monitoring of local-scale environmental changes over the high latitudes, we developed probabilistic land surface FT maps for Alaska using a machine-learning approach and an existing binary FT dataset generated from ALOS PALSAR observations. Model predictors were derived from complementary multi-scale geospatial data, including USGS digital terrain elevations, MODIS land surface temperature and snow cover observations, Landsat8 NDVI, and aboveground biomass. The model FT predictions showed an overall 92% consistency and reproduced the local-scale FT patterns captured by the PALSAR observations across Alaska. The FT retrievals provided more than 250-fold spatial resolution enhancement over other available global satellite FT records derived from passive microwave sensors. Besides FT mapping for land, Planet Skysat videos were used for tracking a variety of ice objects automatically and generating 2-D river flow velocity maps at sub-meter resolution with similar magnitude with ground-based and depth-average measurements (mean difference of ~0.2 m/s). The resulting machine-learning approach and advances in satellite remote sensing, such as high-resolution video observations, allow for improved quantification of the heterogeneity of land/water conditions and more effective monitoring and predictions of the spring flood pulse and river ice breakup in complex boreal and Arctic basins.
Associated Project(s):
Poster Location ID: 2-27
Presentation Type: Poster
Session: Poster Session 2
Session Date: Wed (May 10) 5:15-7:15 PM
CCE Program: TE