IRUS Total

An evaluation of two decades of aerosol optical depth retrievals from MODIS over Australia

File Description SizeFormat 
remotesensing-14-02664-v3.pdfPublished version6.69 MBAdobe PDFView/Open
Title: An evaluation of two decades of aerosol optical depth retrievals from MODIS over Australia
Authors: Shaylor, M
Brindley, H
Sellar, A
Item Type: Journal Article
Abstract: 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).
Issue Date: 2-Jun-2022
Date of Acceptance: 31-May-2022
URI: http://hdl.handle.net/10044/1/97274
DOI: 10.3390/rs14112664
ISSN: 2072-4292
Publisher: MDPI AG
Start Page: 1
End Page: 23
Journal / Book Title: Remote Sensing
Volume: 14
Issue: 11
Copyright Statement: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: 0203 Classical Physics
0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
Publication Status: Published
Open Access location: https://www.mdpi.com/2072-4292/14/11/2664/htm
Online Publication Date: 2022-06-02
Appears in Collections:Faculty of Natural Sciences

This item is licensed under a Creative Commons License Creative Commons