Abstract:Abstract. State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing satellites and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low − especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a Random Forest classifier to automatize the quality assurance/quality control (QA/QC) p… Show more
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