North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)

The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASC...

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Published in:Remote Sensing
Main Authors: Louis Gonzalez, Xavier Briottet
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2017
Subjects:
Q
Online Access:https://doi.org/10.3390/rs9090896
https://doaj.org/article/d9126e0b0c174a16ac1382d844ae25b8
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spelling ftdoajarticles:oai:doaj.org/article:d9126e0b0c174a16ac1382d844ae25b8 2023-05-15T13:06:18+02:00 North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube) Louis Gonzalez Xavier Briottet 2017-08-01T00:00:00Z https://doi.org/10.3390/rs9090896 https://doaj.org/article/d9126e0b0c174a16ac1382d844ae25b8 EN eng MDPI AG https://www.mdpi.com/2072-4292/9/9/896 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs9090896 https://doaj.org/article/d9126e0b0c174a16ac1382d844ae25b8 Remote Sensing, Vol 9, Iss 9, p 896 (2017) SEVIRI sandstorm day and night AOD retrieval North Africa Saudi Arabia Science Q article 2017 ftdoajarticles https://doi.org/10.3390/rs9090896 2022-12-31T11:06:07Z The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube) to: (i) generate day/night remote sensing images in order to detect sandstorms over the Sahara and Saudi Arabia; and (ii) estimate day and night aerosol optical depth (AOD). This paper presents a method to create true color day and night images from the SEVIRI instrument level 1.5 products and the complete operational data processing system to detect sandstorms and quantify the AOD over the desert areas of North Africa and Saudi Arabia. The designed retrieval algorithms are essentially based on the use of artificial neural networks (ANN), which seems to be well suited to this issue. Our methods are validated against two different datasets, namely the Deep Blue NASA moderate-resolution imaging spectroradiometer (MODIS) product and AErosol RObotic NETwork (AERONET) acquisitions located in desert areas. It is shown that NASCube products deliver better estimations for high AOD (>0.2) over land areas than Deep Blue products. The open-public web platform will help researchers to identify, quantify and retrieve the impact of sandstorms over desert regions. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 9 9 896
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic SEVIRI
sandstorm
day and night AOD retrieval
North Africa
Saudi Arabia
Science
Q
spellingShingle SEVIRI
sandstorm
day and night AOD retrieval
North Africa
Saudi Arabia
Science
Q
Louis Gonzalez
Xavier Briottet
North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
topic_facet SEVIRI
sandstorm
day and night AOD retrieval
North Africa
Saudi Arabia
Science
Q
description The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube) to: (i) generate day/night remote sensing images in order to detect sandstorms over the Sahara and Saudi Arabia; and (ii) estimate day and night aerosol optical depth (AOD). This paper presents a method to create true color day and night images from the SEVIRI instrument level 1.5 products and the complete operational data processing system to detect sandstorms and quantify the AOD over the desert areas of North Africa and Saudi Arabia. The designed retrieval algorithms are essentially based on the use of artificial neural networks (ANN), which seems to be well suited to this issue. Our methods are validated against two different datasets, namely the Deep Blue NASA moderate-resolution imaging spectroradiometer (MODIS) product and AErosol RObotic NETwork (AERONET) acquisitions located in desert areas. It is shown that NASCube products deliver better estimations for high AOD (>0.2) over land areas than Deep Blue products. The open-public web platform will help researchers to identify, quantify and retrieve the impact of sandstorms over desert regions.
format Article in Journal/Newspaper
author Louis Gonzalez
Xavier Briottet
author_facet Louis Gonzalez
Xavier Briottet
author_sort Louis Gonzalez
title North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
title_short North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
title_full North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
title_fullStr North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
title_full_unstemmed North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)
title_sort north africa and saudi arabia day/night sandstorm survey (nascube)
publisher MDPI AG
publishDate 2017
url https://doi.org/10.3390/rs9090896
https://doaj.org/article/d9126e0b0c174a16ac1382d844ae25b8
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 9, Iss 9, p 896 (2017)
op_relation https://www.mdpi.com/2072-4292/9/9/896
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs9090896
https://doaj.org/article/d9126e0b0c174a16ac1382d844ae25b8
op_doi https://doi.org/10.3390/rs9090896
container_title Remote Sensing
container_volume 9
container_issue 9
container_start_page 896
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