A geostatistical data fusion technique for merging remote sensing and ground‐based observations of aerosol optical thickness

Abstract

The Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System's Terra satellite have been measuring aerosol optical thickness (AOT) since early 2000. These remote‐sensing platforms complement the ground‐based Aerosol Robotic Network (AERONET) in better understanding the role of aerosols in climate and atmospheric chemistry. To date, however, there have been only limited attempts to exploit the complementary multiangle (MISR) and multispectral (MODIS) capabilities of these sensors along with the ground‐based observations in an integrated analysis. This paper describes a geostatistical data fusion technique that can take advantage of the spatial autocorrelation of the AOT distribution, while making optimal use of all available data sets. Using Level 2.0 AERONET, MISR, and MODIS AOT data for the contiguous United States, we demonstrate that this approach can successfully incorporate information from multiple sensors and provide accurate estimates of AOT with rigorous uncertainty bounds. Cross‐validation results show that the resulting AOT product is closer to the ground‐based AOT observations than either of the individual satellite measurements.