Dust storms are one of the natural hazards whose incidence has increased in the recent years over Sahara desert, Australia and northern China. Thus, it is important to know the causation, movement and radiation effects of dust storms. Satellite remote sensing is the most common method for monitoring Dust Storms but its use over sandy ground is still limited as they have similar characteristics. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data. However, there have been fewer studies for detecting dust storms at night. The key elements of this study are to use a back-propagation artificial neural network with Brightness Temperature of band 31 and four Brightness Temperature Differences calculated using data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop a method for detecting dust storms during both day and night. Results have shown that the method can detect dust storms at both day and night and also over different land surfaces. 

Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave …

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

One of the problems worsened by climate change is the occurrence of Sand Dust Storms (SDS), which are dry winds carrying sand. In recent years the numbers of SDSs have increased in North Africa, Australia and northern China. Satellite remote sensing (SR) is the main method for monitoring SDS as they happen. However its use for identifying sand dust storms over sandy ground such as Saharan desert is still limited as both materials share similar characteristics. In this paper, we propose a new approach to distinguish dust storms cloud from sandy ground and water cloud using Moderate Resolution Imaging Spectroradiometer (MODIS) bands.