Evaluation of passive microwave snow water equivalent algorithms in the depth hoar-dominated snowpack of the Kuparuk River Watershed, Alaska, USA

• Published in: Remote sensing of environment Vol. 93; no. 4; pp. 511 - 527
• Main Authors: Koenig, Lora S., Forster, Richard R.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.12.2004; Elsevier Science
• Subjects: Alaska; Applied geophysics; Arctic; Depth hoar; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hydrology; Hydrology. Hydrogeology; Internal geophysics; Passive microwave; Snow water equivalent (SWE); Alaska; United States; Arctic region; North Slope; Depth hoar; Alaska; Arctic; Passive microwave; Snow water equivalent (SWE); microwaves; rivers; algorithms; ground truth; accuracy; remote sensing; North America; spatial distribution; meltwater; evaluation; drainage basins; instruments; depth; snow; polar regions; errors; Pixel
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2004.08.004

This research investigates the utility of passive microwave remote sensing instruments to accurately determine snow water equivalent (SWE) over large spatial extents. Three existing Special Sensor Microwave Imager (SSM/I) snow water equivalent algorithms produced by Chang, Tait and Goodison were evaluated for their ability to determine snow water equivalent in a snowpack containing substantial depth hoar, large faceted snow crystals. The Kuparuk River Watershed (8140 km 2) test site on the North Slope of Alaska was chosen for its snowpack containing a think depth hoar layer and long history of ground truth data. A new regional snow water equivalent algorithm was developed to determine if it could produce better results than the existing algorithms in an area known to contain significant depth hoar. The four algorithms were tested to see how well they could determine snow water equivalent: (1) on a per pixel basis, (2) across swath-averaged spatial bands of approximately 850 km 2, and (3) on a watershed scale. The algorithms were evaluated to see if they captured the annual spatial distribution in snow water equivalent over the watershed. Results show that the algorithms developed by Chang and from this research are generally within 3 cm of the spatially averaged snow water equivalents over the entire watershed. The algorithms produced by Chang, Tait, and in this research were able to predict the basin-wide ground measured snow water equivalent value within a percent error range from −32.4% to 24.4% in the years with a typical snowpack. None of the algorithms produce accurate results on a pixel-by-pixel scale, with errors ranging from −26% to 308%.