MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations

TLDR

The MODIS Collection 6 aerosol product suite now includes Enhanced Deep Blue, Dark Target, and a merged algorithm, with DB expanded to vegetated land and all three refined for Aqua data. This study provides a reference for MODIS users to assess the global and regional characteristics of the C6 DB, DT, and merged datasets. The authors validate the datasets against AERONET observations at 111 sites, performing regional and surface/aerosol‑type analyses. No single algorithm consistently outperforms the others; in many regions all three are suitable, while in some cases one algorithm is superior, leading to regionally dependent usage recommendations.

Abstract

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over‐water algorithm. All three have been refined in the recent “Collection 6” (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Additionally, a new “merged” data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of reference for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) analysis. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quantitative applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.