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The Aerosol Optical Depth (AOD) retrieved from satellite remote sensing measurements such as from MISR and MODIS, both onboard the Terra platform, are widely used for studying regional and global patterns of aerosol loading. Aerosol products from these sensors are also used for analyzing feedbacks and relationship between aerosols and climatic variables including clouds, precipitation, and radiation fluxes. Several statistical techniques leading to the understanding of such relationships, including empirical orthogonal function and temporal trend extraction methods, require spatially complete AOD data records. Inherent to remote sensing of aerosols, cloud cover significantly affects aerosol retrievals and results in missing data across the AOD products. This paper demonstrates widely-used geostatistical techniques, such as Co-Kriging (CK) and Regression Kriging (RK), for spatially-filling missing data in the MISR AOD product for the period 2001-2013. Among the unique characteristics of this data-filling algorithm is that it utilizes additional AOD information obtained from MODIS. The mean accuracy of the predicted MISR AOD using CK method is estimated to be 0.05, globally. The gap-filled MISR AOD data are also compared with 131 ground-based Aerosol Robotic Network (AERONET) stations, located around the world. It is found that Root Mean Squared Error of the gap-filled AOD dataset and the original MISR AOD product with respect to AERONET data are 0.143. The gap-filled AOD dataset can be used in applications where the presence of missing values is undesirable such as for global/regional aerosol variability and trend analysis

Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer