Statistical Mapping of air Quality by Remote Sensing

Alessandro Fassd* and Francesco Finazzi
University of Bergamo - Dept. of Information Technology and Mathematical Methods Bergamo,Italy
*alessandro.fasso@unibg.it

Abstract: In this paper we consider the multivariate dynamic coregionalization model which has recently been introduced in environmental spatio-temporal statistics. The main modelling objective here is dynamic mapping of airborne particulate matters (PM10) by merging measurements from an irregularly spaced ground level monitoring network and regularly spaced satellite measurements of aerosol optical thickness (AOT). Due to the fact that AOT measurements are not available under cloudy conditions, we have to manage a large amount of missing data. In principle this task is naturally handled by the state space representation of the model and the maximum likelihood estimation through the EM algorithm. After discussing the uncertainty sources of this model, we check the model sensitivity to missingness in the case of the "padano- veneto" region, North Italy, including the Alps. To do this, an extensive simulation campaign is performed with missing rate ranging from 0% to 90% and showing the reliability of the method for the case under study.

Keywords: dynamic coregionalization model; aerosol optical thickness; multivariate spatio-temporal modeling; EM algorithm

AttachmentSize
FassoAccuracy2010.pdf598.8 KB