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.

A Novel Approach to Snow Coverage Retrieval Under Cloud-Obscured Pixels Based on Multitemporal Correlation

Niroumand-Jadidi, Milad;Bovolo, Francesca
2019

Abstract

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.
978-1-5386-9154-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/320206
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