2018
DOI: 10.3390/rs10121993
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Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

Abstract: Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODI… Show more

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Cited by 24 publications
(8 citation statements)
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“…The reference data are also used as labeling data in machine learning-based method. The dust storm detection results can be assessed visually (Miller et al, 2017;Xie et al, 2017;Yan et al, 2020;Yue et al, 2017), and validated against MODIS AOD products (Yan et al, 2020;Yue et al, 2017), OMI Ultra Violet Aerosol Index (UVAI) (Xie et al, 2017), CALIPSO dust type (Prachi and Pravin, 2014;Shi et al, 2018;Xie et al, 2017), AERONET products (Bin Abdulwahed et al, 2018;Butt and Mashat, 2018a), or local meteorological station-based observation using visibility data (Albugami et al, 2018;Butt and Mashat, 2018a;Miller et al, 2017;Samadi et al, 2014;Taghavi et al, 2017).…”
Section: Validation Of Dust Presencementioning
confidence: 99%
“…The reference data are also used as labeling data in machine learning-based method. The dust storm detection results can be assessed visually (Miller et al, 2017;Xie et al, 2017;Yan et al, 2020;Yue et al, 2017), and validated against MODIS AOD products (Yan et al, 2020;Yue et al, 2017), OMI Ultra Violet Aerosol Index (UVAI) (Xie et al, 2017), CALIPSO dust type (Prachi and Pravin, 2014;Shi et al, 2018;Xie et al, 2017), AERONET products (Bin Abdulwahed et al, 2018;Butt and Mashat, 2018a), or local meteorological station-based observation using visibility data (Albugami et al, 2018;Butt and Mashat, 2018a;Miller et al, 2017;Samadi et al, 2014;Taghavi et al, 2017).…”
Section: Validation Of Dust Presencementioning
confidence: 99%
“…Due to Shamal winds, the areas mentioned above are typically experiencing dust in the spring and summer. [55,26]. MODIS has 36 bands in the visible to thermal infrared spectrum (0.4 -14.4 ”m).…”
Section: Study Areamentioning
confidence: 99%
“…By considering the surface background, various algorithms have been developed, e.g., Dark Target for detecting dust on the sea surface [39] and Deep Blue for bright surfaces such as deserts [40,41,42]. Moreover, a variety of approaches based on different parts of the electromagnetic spectrum are proposed, including, thermal-based bands [43,44,45,46,47,48,49], visible-and near infrared-based bands [50,51], and combination of visible and infrared spectral bands [52,53,25,54,55,10]. Many studies focused on the temporal and spatial variability of dust aerosol frequency [33], while others concentrate on identifying dust source regions [56].…”
Section: Introductionmentioning
confidence: 99%
“…Among the methods developed to identify ash/dust plumes from space [18][19][20][21][22][23], two robust satellite techniques (RST) based algorithms, running on both polar and geostationary satellite data, were used with success in different geographic areas and under different observational conditions [24][25][26][27][28][29]. These algorithms were tailored in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project (http://www.eunadics.eu/), which aims at bridging the gap in European-wide data and information availability during airborne hazards.…”
Section: Introductionmentioning
confidence: 99%