Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil

• Published in: Atmospheric environment (1994) Vol. 314; p. 120130
• Main Authors: Rudke, Anderson Paulo, Martins, Jorge Alberto, Martins, Leila Droprinchinski, Vieira, Carolina Letícia Zilli, Li, Longxiang, Assunção da Silva, Carlos Fabricio, dos Santos, Alex Mota, Koutrakis, Petros, de Almeida Albuquerque, Taciana Toledo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: AOD; Biomass burning emissions; Land cover type dependency; MODIS; View geometry dependence; Biomass burning emissions; View geometry dependence; AOD; MODIS; Land cover type dependency
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2023.120130

Brazil experiences significant aerosol loads throughout the year, particularly during the biomass-burning season in the Amazon. Thus, given the importance of aerosols for climate and health, this research aimed to validate and compare Aerosol Optical Depth (AOD) products over Brazil. This evaluation considers three algorithms that retrieve AOD by using data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor: Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC). To validate the satellite data, 17 sunphotometers from the AErosol RObotic NETwork (AERONET) were utilized. The results show a high correlation (R > 0.9) between the MODIS-AOD products and ground-based data. However, MODIS-AOD products tend to overestimate or underestimate AOD values, depending on the specific AOD value and algorithm evaluated. Additionally, it was observed that the performance of the algorithms is influenced by factors such as land cover type, view geometry, and the spatiotemporal distribution of aerosols. In particular, challenges were encountered when retrieving robust AOD data for Savanna and Urban cover classes. In conclusion, the results indicate that MAIAC and DB algorithms demonstrate greater stability in retrieving AOD values. Nevertheless, caution should be exercised when applying these products to map aerosols on specific surfaces, such as urban areas. •High aerosol loads are observed in the Amazon rainforest during spring.•Aerosol Optical Depth (AOD) algorithms have shown good correlations (>0.9).•DT underestimates low AOD values and overestimates high ones; DB and MAIAC have inverse behavior.•Land cover, angular geometry, and spatiotemporal patterns impact the algorithms differently.•DT performed best over forests and urban areas; MAIAC and DT were more robust elsewhere.