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Main Title: A Novel Data Fusion Technique for Snow Cover Retrieval
Author(s): De Gregorio, Ludovica
Callegari, Mattia
Marin, Carlo
Zebisch, Marc
Bruzzone, Lorenzo
Demir, Begüm
Strasser, Ulrich
Marke, Thomas
Günther, Daniel
Nadalet, Rudi
Notarnicola, Claudia
Type: Article
Language Code: en
Abstract: This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season.
Issue Date: 27-Jun-2019
Date Available: 25-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): data fusion
snow model
machine learning
remote sensing
Journal Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Volume: 12
Issue: 8
Publisher DOI: 10.1109/JSTARS.2019.2920676
Page Start: 2862
Page End: 2877
EISSN: 2151-1535
ISSN: 1939-1404
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