Statistical data fusion of multi-sensor AOD over the Continental United States |
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| Authors: | Sweta Jinnagara Puttaswamy Hai M Nguyen Amy Braverman Xuefei Hu |
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| Institution: | 1. Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, GA, USA.;2. Jet Propulsion Laboratory, Pasadena, CA, USA. |
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| Abstract: | This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N3), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States. |
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| Keywords: | aerosol optical depth MODIS GOES AERONET universal kriging data fusion |
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