A Novel Approach To Snow Coverage Retrieval Under Cloud-Obscured Pixels Based On Multitemporal Correlation

This paper introduces a novel method for estimation of snow/no-snow labels for cloud-obscured pixels in order to enable an accurate mapping of the snow-covered area (SCA) in time series. The proposed method leverages the embedded information in multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, date of acquisition, and the cloud obscuration duration. The proposed method is built upon three main steps: i) classification of single date images into three classes (snow, no-snow, and cloud), ii) estimation of conditional probabilities of class-transition in relation with the environmental factors, and iii) prediction of the snow/no-snow labels for the cloud-obscured pixels. We validated the proposed method on daily MODIS images acquired over 10 years in a mountain area located in Italy and Austria. The proposed method yielded SCA improved maps compared to a standard method of assigning labels beneath the clouds.