Comparison of dust source identification techniques over land in the Middle East region using MODIS data

Karimi, Neamat; Moridnejad, Ali; Golian, Saeed; Samani, Jamal Mohammad Vali; Karimi, Danesh; Javadi, Saman

doi:10.5589/m12-048

Cited by 57 publications

(29 citation statements)

References 49 publications

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“…Whereas TOMS-OMI AI is only capable of retrieving absorbing aerosols, MODIS AOD measurements are sensitive to both absorbing and non-absorbing aerosols. The Deep Blue algorithm employs radiances in the blue spectrum, where the surface reflectance is very low, able to detect the presence of aerosols by an increase of total reflectance and enhanced spectral contrast (Ginoux et al, 2012;Karimi et al, 2012). Deep Blue AOD 550 retrievals have been compared to those obtained from AERONET over arid-desert locations exhibiting a satisfactory agreement for the majority of the sites (Ginoux et al, 2012) …”

Section: Toms/omi Aerosol Indexmentioning

confidence: 99%

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

et al. 2013

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Satellite remote sensing provides important observational constraints for monitoring dust life cycle and improving the understanding of its effects on local to global scales. The present work analyses the dust-aerosol patterns over the arid environment of the Sistan region-in southeastern Iran, by means of multiple satellite platforms aiming to reveal the spatiotemporal distribution and trends. The dataset includes records of Aerosol Index (AI) from Total Ozone Mapping Spectrometer (TOMS) and 6-year AI records from the Ozone Monitoring Instrument (OMI) aboard Aura. Moreover, the aerosol optical depth (AOD) is analyzed through 11-year records from Multi-angle Imaging Spectro-Radiometer (MISR) aboard Terra (2000Terra ( -2010 and from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra (2000Terra ( -2007 and Aqua (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). The main focus is to determine similarities and differences in dust variability over southwest Asia, in general, and the Sistan region, in particular. The results show a marked seasonal cycle with high aerosol loading during summer and lower in winter, while MISR, MODIS and TOMS/OMI observations agree in both terms of monthly and seasonally mean spatial and temporal patterns. The higher aerosol concentrations during summer are interpreted as a result of the combined effect of the seasonal drying of the Hamoun lakes and the strong northerly Levar winds favoring dust erosion from the alluvial deposits in Sistan . After prolonged drought period, the dust-aerosol load over the area has increased in the beginning of the 2000s and decreased after 2004, 2 thereby leading to an overall declining trend during the last decade. Such a trend is absent during the winter period when dust emission over the region is minimal.

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Section: Toms/omi Aerosol Indexmentioning

confidence: 99%

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

et al. 2013

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“…[3] The combination of two instruments in flight (aboard the Terra satellite from 2000 onward and Aqua from 2002 onward), and a wide swath giving daily near-global observations, make the MODIS sensors an attractive choice for such an aerosol dataset. Deep Blue data have since been wellused for various applications, such as identification of dust sources [Ginoux et al, 2012;Karimi et al, 2012;Schepanski et al, 2012], evaluation/development of chemistry transport models [Draxler et al, 2010;Laurent et al, 2010;Wang et al, 2012], and comparison with other satellite-based dust aerosol datasets [DeSouza-Machado et al, 2010;Carboni et al, 2012]. Although other instruments on polar-orbiting satellites, notably the Multiangle Imaging Spectroradiometer (MISR) [Martonchik et al, 1998] and Along-Track Scanning Radiometer (ATSR) [North, 2002;Sayer et al, 2012a] series, are also able to retrieve AOD over deserts, they suffer from a more limited spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data

et al. 2013

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[1] The "Deep Blue" aerosol optical depth (AOD) retrieval algorithm was introduced in Collection 5 of the Moderate Resolution Imaging Spectroradiometer (MODIS) product suite, and complemented the existing "Dark Target" land and ocean algorithms by retrieving AOD over bright arid land surfaces, such as deserts. The forthcoming Collection 6 of MODIS products will include a "second generation" Deep Blue algorithm, expanding coverage to all cloud-free and snow-free land surfaces. The Deep Blue dataset will also provide an estimate of the absolute uncertainty on AOD at 550 nm for each retrieval. This study describes the validation of Deep Blue Collection 6 AOD at 550 nm ( M ) from MODIS Aqua against Aerosol Robotic Network (AERONET) data from 60 sites to quantify these uncertainties. The highest quality (denoted quality assurance flag value 3) data are shown to have an absolute uncertainty of approximately (0.086+0.56 M )/AMF, where AMF is the geometric air mass factor. For a typical AMF of 2.8, this is approximately 0.03+0.20 M , comparable in quality to other satellite AOD datasets. Regional variability of retrieval performance and comparisons against Collection 5 results are also discussed.

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“…However, this is compensated by the much higher spatial and spectral resolution of MODIS, which has 36 spectral channels and generates imagery at spatial resolutions 250 m, 500 m and 1 km, depending on the channel [23]. Hence, several previous studies have developed and described dust-detection tests based on MODIS data [24][25][26][27][28]. Consequently, the aim of this study is to evaluate which of the existing MODIS dust detection methods are most effective over the Arabian Peninsula.…”

Section: Previous Workmentioning

confidence: 99%

Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

et al. 2018

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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 MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31-32); (c) BTD (20-31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for 'dust-present' versus 'dust-free' conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31-32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20-31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types.

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Comparison of dust source identification techniques over land in the Middle East region using MODIS data

Cited by 57 publications

References 49 publications

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations

Validation and uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data

Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

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