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dc.contributor.authorDi, Aojie
dc.contributor.authorXue, Yong
dc.contributor.authorYang, Xihua
dc.contributor.authorLeys, John
dc.contributor.authorGuang, Jie
dc.contributor.authorMei, Linlu
dc.contributor.authorWang, Jingli
dc.contributor.authorShe, Lu
dc.contributor.authorHu, Yincui
dc.contributor.authorHe, Xingwei
dc.contributor.authorChe, Yahui
dc.contributor.authorFan, Cheng
dc.date.accessioned2016-11-15T12:45:32Z
dc.date.available2016-11-15T12:45:32Z
dc.date.issued2016-08-26
dc.identifier.citationDi, A. et al, (2016) 'Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations', Remote Sensing, 8 (9):702en
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs8090702
dc.identifier.urihttp://hdl.handle.net/10545/620858
dc.description.abstractThe Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.
dc.language.isoenen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.relation.urlhttp://www.mdpi.com/2072-4292/8/9/702en
dc.rightsArchived with thanks to Remote Sensingen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectRemote Sensingen
dc.subjectAerosol optical depthen
dc.subjectAerosol typeen
dc.subjectGeostationary satelliteen
dc.titleDust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observationsen
dc.typeArticleen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalRemote Sensingen
refterms.dateFOA2019-02-28T14:55:01Z
html.description.abstractThe Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.


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