Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

Albugami, Sarah; Palmer, S. J.; Meersmans, Jeroen; Waine, Toby

doi:10.3390/rs10121993

Cited by 24 publications

(6 citation statements)

References 53 publications

(129 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Review of dust storm detection algorithms for multispectral satellite sensors

Wong

Lee

et al. 2021

Atmospheric Research

View full text Add to dashboard Cite

Section: Validation Of Dust Presencementioning

confidence: 99%

Review of dust storm detection algorithms for multispectral satellite sensors

Wong

Lee

et al. 2021

Atmospheric Research

View full text Add to dashboard Cite

“…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 µm).…”

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%

Efficient dust detection based on spectral and thermal observations of MODIS imagery

Bahrami

Homayouni

Shah-Hosseini

et al. 2020

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region over the previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by the seasonal and occasional dust storm from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery. MODIS imagery possesses impressive spectral and temporal characteristics that are essential for such an environmental application of Earth observations. An efficient algorithm, based on the spectral and statistical analysis of both thermal and reflectance bands of MODIS data, was developed through a decision tree method. To this end, an index was proposed to detect the dusts over the land using the brightness temperature of thermal bands. The results of the proposed algorithm were assessed utilizing ground-based observation of synoptic stations. The proposed method showed high reliability and performance, as well as the automatic capability of dust detection in land and sea areas of the image simultaneously. The evaluation of results showed that the proposed algorithm could detect thin and thick dust storms with an overall accuracy of about 80%. Moreover, the dust monitoring results visually agreed well with the Ozone Monitoring Instrument Aerosol Index (OMI-AI) dust products.

show abstract

“…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%

Validation of Ash/Dust Detections from SEVIRI Data Using ACTRIS/EARLINET Ground-Based LIDAR Measurements

et al. 2020

View full text Add to dashboard Cite

Two tailored configurations of the Robust Satellite Technique (RST) multi-temporal approach, for airborne volcanic ash and desert dust detection, have been tested in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. The two algorithms, running on Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data, were previously assessed over wide areas by comparison with independent satellite-based aerosol products. In this study, we present results of a first validation analysis of the above mentioned satellite-based ash/dust products using independent, ground-based observations coming from the European Aerosol Research Lidar Network (EARLINET). The aim is to assess the capabilities of RST-based ash/dust products in providing useful information even at local scale and to verify their applicability as a “trigger” to timely activate EARLINET measurements during airborne hazards. The intense Saharan dust event of May 18–23 2008—which affected both the Mediterranean Basin and Continental Europe—and the strong explosive eruptions of Eyjafjallajökull (Iceland) volcano of April–May 2010, were analyzed as test cases. Our results show that both RST-based algorithms were capable of providing reliable information about the investigated phenomena at specific sites of interest, successfully detecting airborne ash/dust in different geographic regions using both nighttime and daytime SEVIRI data. However, the validation analysis also demonstrates that ash/dust layers remain undetected by satellite in the presence of overlying meteorological clouds and when they are tenuous (i.e., with an integrated backscatter coefficient less than ~0.001 sr−1 and with aerosol backscatter coefficient less than ~1 × 10−6 m−1sr−1). This preliminary analysis confirms that the continuity of satellite-based observations can be used to timely “trigger” ground-based LIDAR measurements in case of airborne hazard events. Finally, this work confirms that advanced satellite-based detection schemes may provide a relevant contribution to the monitoring of ash/dust phenomena and that the synergistic use of (satellite-based) large scale, continuous and timely records with (ground-based) accurate and quantitative measurements may represent an added value, especially in operational scenarios.

show abstract

Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

Cited by 24 publications

References 53 publications

Review of dust storm detection algorithms for multispectral satellite sensors

Review of dust storm detection algorithms for multispectral satellite sensors

Efficient dust detection based on spectral and thermal observations of MODIS imagery

Validation of Ash/Dust Detections from SEVIRI Data Using ACTRIS/EARLINET Ground-Based LIDAR Measurements

Contact Info

Product

Resources

About