Snow observation from space: An approach to improving snow cover detection using four decades of Landsat and Sentinel-2 imageries across Switzerland

Abstract

Landsat and Sentinel-2 satellites offer significant advantages for monitoring snow cover over mountainous countries like Switzerland. Starting in the 1970s, Landsat data provides over 50 years of medium resolution imagery. However, the main limitation of optical imagery is cloud cover. Cloud obstruction is particularly challenging for Landsat and Sentinel-2 data, which have limited temporal resolutions. In this study we present the Snow Observation from Space (SOfS) algorithm composed of seven successive temporal and spatial techniques to reduce cloud coverage in the final snow cover products. We used long-term Landsat and Sentinel-2 datasets available from the Swiss Data Cube. The results indicate that the filtering techniques are efficient in reducing cloud cover by half while still leaving an average of less than 30% of cloud cover. The accuracy of the entire algorithm is evaluated over Switzerland, using in-situ measurements of 263 climate stations in the period 1984-2021. The validation results show an agreement between SOfS dataset and ground snow observations with an average accuracy of 93%. • Highlights are a short collection of bullet points that should capture the novel results of your research as well as any new methods used during your study. Highlights will help increase the discoverability of your article via search engines. Some guidelines: • Innovative snow cover dataset using medium-resolution data from 1984-2021 • Cloud cover reduced by nearly 50% using a multi-step filtering process • Overall accuracy of 95% validated with in-situ snow depth measurements

Topics & Concepts