An evaluation of satellite dust-detection algorithms in the Middle East region

Abstract

In recent years, the frequency, spatial extent and intensity of dust storms has increased and it is one of the main continuously occurring environmental hazard in the Middle East region. Since dust storms generally cover a large spatial extent and are highly dynamic, satellite Earth Observation is a key tool for detecting their occurrence, identifying their origin and monitoring their transport and state. A variety of satellite dust detection algorithms have been developed to identify dust emissions sources and dust plumes once entrained in the atmosphere. This paper evaluates the performance of five widely applied dust detection algorithms: the Brightness Temperature Difference (BTD), D-parameter, Normalized Difference Dust Index (NDDI), Thermal-Infrared Dust Index (TDI) and the Middle East Dust Index (MEDI). These algorithms are applied to MODIS (MOD21KM) data to detect dust-contaminated pixels during three significant dust events in 2007 in the Middle East region that originated from sources in Iraq, Syria and Saudi Arabia. The results indicate that all methods have a comparable performance in detecting dust-contaminated pixels during the three dust events with an average detection rate (between all algorithms) of 85%. However, substantial differences exist in their ability to distinguish dust from clouds and the land surface, which resulted in large errors of commission. Direct validation of these algorithms with observations from seven Aerosol Robotic Network (AERONET) stations in the region found an average false detection rate (between all algorithms) of 89.6%. Although the algorithms performed well in detecting the dust contaminated pixels their high false detection rate means it is challenging to apply these algorithms in operational context.

Particulate Matter Concentrations in a Middle Eastern City – An Insight to Sand and Dust Storm Episodes

“…The Eastern Mediterranean region also suffers from SDS episodes, which have been reported more frequently during the past decades (Hamidi et al, 2013;Keramat et al, 2011;Kutiel et al, 2003;Hussein et al, 2011Hussein et al, , 2014Hussein et al, , 2017Hussein et al, , and 2018Kchih et al, 2015;Munir et al, 2017;Amarloei et al, 2019;Abdulwahed et al, 2019;Saeifar et al, 2019;Fountoukis et al, 2020). The increased frequency of SDS episodes in the Eastern Mediterranean region has been referred to the impact of climate change and its consequences by desertification, deforestation, wetland destruction, increased population growth and anthropogenic emissions, food insecurity, and water…”

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

“…12-b). Bin Abdulwahed, Dash and Roberts [5] evaluated various dust detection algorithms in the Middle East [5]. Their results showed that the Middle East Dust Index (MEDI) had difficulty distinguishing dust from dark and deserts regions.…”

“…According to the World Meteorological Organization (WMO), the dust particles affect the cloud droplets and crystals, thus affecting the location and amount of precipitation. Therefore, the effects of dust on drought and the environment and climate change must be carefully assessed [5].…”

“…The ground-based observations are among the most accurate technologies; nevertheless, they are unable to monitor the displacement of dust on a large-scale. The properties of dust particles are frequently measured by ground measurements using sun photometers [5]. The AERONET (AErosol RObotic NETwork) is a network of ground-based sun photometers that provide high temporal resolution Aerosol Optical Depth (AOD) measurements [21].…”

“…Besides, satellite imagery can be efficient in studying how the meteorological parameters such as wind speed, wind direction, atmospheric pressure, and surface temperature affect the rise and distribution of dust in time and space [22,23,24]. Dust can be detected in the ultraviolet range by absorption (0.315 -0.4 µm), in the visible spectrum by scattering and in the thermal infrared region by the difference of ground surface/aerosol emissivity [25,26,5,27,28].…”