Imperial College London
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Professor Helen Brindley

Faculty of Natural Sciences, Department of Physics

Professor in Earth Observation
 
 
 
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Contact

 

+44 (0)20 7594 7673h.brindley

 
 
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Location

 

717Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Shaylor:2022:10.3390/rs14112664,
author = {Shaylor, M and Brindley, H and Sellar, A},
doi = {10.3390/rs14112664},
journal = {Remote Sensing},
pages = {1--23},
title = {An evaluation of two decades of aerosol optical depth retrievals from MODIS over Australia},
url = {http://dx.doi.org/10.3390/rs14112664},
volume = {14},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).
AU  - Shaylor,M
AU  - Brindley,H
AU  - Sellar,A
DO  - 10.3390/rs14112664
EP  - 23
PY  - 2022///
SN  - 2072-4292
SP  - 1
TI  - An evaluation of two decades of aerosol optical depth retrievals from MODIS over Australia
T2  - Remote Sensing
UR  - http://dx.doi.org/10.3390/rs14112664
UR  - https://www.mdpi.com/2072-4292/14/11/2664
UR  - http://hdl.handle.net/10044/1/97274
VL  - 14
ER  -
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