Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model

This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Stavros Kolios, Nikos Hatzianastassiou
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
Dust detection
Meteosat satellite
remote sensing
artificial neural networks
Mediterranean
AERONET
Science
Q
Aerosol Robotic Network
Online Access:https://doi.org/10.3390/rs11091022
https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e
Description
Summary:This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD 550 nm ) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD 550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD 550 nm values, a Pearson correlation coefficient (r P ) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD 550 nm values.

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