Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery

The estimation of a snow-covered area (SCA) is often achieved by classification of imagery acquired by passive optical sensors aboard satellite platforms with high revisit frequencies [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)] required by various applications. The extraction of the SCA from optical imagery is inevitably hindered by the presence of clouds, where the surface labels (i.e., snow and no-snow) remain unknown. In the bulk of existing research, cloud pixels are either masked out without any further processing or assigned to snow/no-snow classes by performing spatial or temporal filters. The current approaches to deal with the cloud-obscuration problem are subjected to sizable uncertainties. They mostly neglect or only partially account for the temporal correlation, which undermines the full potential of long time series. We propose a novel method for estimating snow/no-snow labels beneath the clouds that leverages the multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, the date of acquisition (and thus the season), and the cloud-obscuration duration. The proposed method is built upon analyzing the long time series of maps derived from the single date classification of images in order to estimate the conditional probabilities of transition between the snow and no-snow classes. The probabilities are estimated as a function of the aforementioned environmental and multitemporal factors, which allow for the prediction of labels beneath the clouds in either archive or new acquisitions. Validation results on a four-year time series of daily MODIS images acquired over the Euregio region in Italian and Austrian Alps prove the effectiveness and robustness of the proposed method in assigning labels beneath the clouds.