A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products. (August 2015) - Xu, H.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provide validated aerosol optical depth (AOD) products over both land and ocean. However, the values of the AOD provided by each of these satellites may show spatial and temporal differences due to the instrument characteristics and aerosol retrieval algorithms used for each instrument. In this article we present a method to produce an AOD data set over Asia for the year 2007 based on fusion of the data provided by different instruments and/or algorithms. First, the bias of each satellite-derived AOD product was calculated by comparison with ground-based AOD data derived from the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing NETwork (CARSNET) for different values of the surface albedo and the AOD. Then, these multiple AOD products were combined using the maximum likelihood estimate (MLE) method using weights derived from the root mean square error (RMSE) associated with the accuracies of the original AOD products. The original and merged AOD dataset has been validated by comparison with AOD data from the CARSNET. Results show that the mean bias error (MBE) and mean absolute error (MAE) of the merged AOD dataset are not larger than that of any of the original AOD products. In addition, for the merged AOD dataset the fraction of pixels with no data is significantly smaller than that of any of the original products, thus increasing the spatial coverage. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms. Highlights: A fusion method to generate an AOD dataset from five different AOD products. A Consistent AOD Dataset over China by Integrating of Various AOD Products. The merged AOD dataset has high overall accuracy (RMSE = 0.16). The merged AOD estimates are closer to the reference field than others.