Gaussian Conditional Random Fields for Aggregation of Operational Aerosol Retrievals | IEEE Journals & Magazine | IEEE Xplore

Gaussian Conditional Random Fields for Aggregation of Operational Aerosol Retrievals


Abstract:

We present a Gaussian conditional random field model for the aggregation of aerosol optical depth (AOD) retrievals from multiple satellite instruments into a joint retrie...Show More

Abstract:

We present a Gaussian conditional random field model for the aggregation of aerosol optical depth (AOD) retrievals from multiple satellite instruments into a joint retrieval. The model provides aggregated retrievals with higher accuracy and coverage than any of the individual instruments while also providing an estimation of retrieval uncertainty. The proposed model finds an optimal temporally smoothed combination of individual retrievals that minimizes the root-mean-squared error of AOD retrieval. We evaluated the model on five years (2006-2010) of satellite data over North America from five instruments (Aqua and Terra MODIS, MISR, SeaWiFS, and the Ozone Monitoring Instrument), collocated with ground-based Aerosol Robotic Network ground-truth AOD readings, clearly showing that the aggregation of different sources leads to improvements in the accuracy and coverage of AOD retrievals.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 12, Issue: 4, April 2015)
Page(s): 761 - 765
Date of Publication: 17 October 2014

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