Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China

Abstract

Two distinct algorithms, namely Deep Blue (DB) and Multiangle Implementation of Atmospheric Correction (MAIAC), based on the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide aerosol optical depth (AOD) products over bright surfaces. Recently, the newest MAIAC Collection 6.1 (C6.1) product was available, which corrected many known deficiencies of C6. Due to the significant impact of high-reflectivity surfaces on the accuracy of aerosol retrieval, long-term validation and comprehensive assessment of AOD over bright surfaces are required at a regional scale. Firstly, this study conducted a systematic evaluation of three products against ground-based Sunphotometer data obtained from 21 sites of the China Aerosol Remote Sensing Network (CARSNET) and 2 sites of the Aerosol Robotic Network (AERONET) from the year 2002 to 2014. The validation results showed a significant underestimation in DB and MAIAC AOD retrievals as indicated by the large percent of retrievals below the expected error (EE) envelope, and significant negative biases. In comparison, both versions of MAIAC products performed better than DB product, which was also revealed by the site scale validation. Besides, the performance of the C6.1 MAIAC was slightly improved over the study region compared to the C6 MAIAC. Finally, this study made an in-depth investigation of the underestimation of AOD products affected by various factors, i.e., aerosol loading, land type, NDVI, surface albedo, and scattering angle. The results indicated that the large negative bias of AOD retrievals for the three products tended to occur in the case of large aerosol loadings and high bright surfaces, which could be attributed to the inaccurate assumption of aerosol models and the insensitivity of top of atmosphere (TOA) reflectance to AOD variations caused by the high reflectivity of the underlying surface. The findings of this study are expected to provide some reference for the improvement of aerosol retrieval algorithms over bright surfaces.