Dust storm detection using random forests and physical-based approaches over the Middle East

Abstract

Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic studies. Detection of dust storms is critical to accurately understand dust, their properties and distribution. Currently, remotely sensed data such as MODIS (Moderate Resolution Imaging Spectroradiometer) with appropriate temporal and spectral resolutions have been widely used for this purpose. This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface. Therefore, classification-based approaches could be deployed as an alternative. In this paper, the most relevant bands are chosen based on the physical effects of the major classes, particularly dust, cloud and snow, on both emissive infrared and reflective bands. In order to verify the capability of the methods, OMAERUV AAOD (aerosol absorption optical depth) product from OMI (Ozone Monitoring Instrument) sensor is exploited. In addition, some small regions are selected manually to be considered as ground truth for measuring the probability of false detection (POFD) and probability of missing detection (POMD). The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images. Quantitatively, the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7% and 6% of POFD and POMD respectively. Moreover, results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously. The performance of the physical-based approaches is found weaker than RF due to empirical thresholds that are not always true.

Machine Learning Applications to Dust Storms: A Meta-Analysis

“…The tools used for ground observation include video surveillance, observation towers, and remote sensors, such as radar or Lidar sensors. Dust storm prediction-related studies collect extensive data for about 10 years for specific cities, which is Geostationary Radiation Imager (AGRI) (Berndt et al, 2021;Chacon-murguía et al, 2011;Ebrahimi-khusfi, et al, 2021b;Ebrahimi-khusfi, et al, 2021a;El-ossta et al, 2013;Hou, Wu, et al, 2020;Lee et al, 2021;Ma et al, 2015;Nabavi et al, 2018;Qing-dao-er-ji et al, 2020;Rivasperea et al, 2015;Rivas-perea et al, 2010;Shahrisvand and Akhoondzadeh, 2013;Shi et al, 2020;Shi et al, 2019;Souri and Vajedian, 2015;Tiancheng et al, 2019;Wang et al, 2022;Xiao et al, 2015).…”

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

“…developed a physically-based algorithm to detect dust from MODIS and used 14 out of 36 MODIS bands. Although these bands can provide sufficient information content for dust detection, it is hard to tell how much information can be added if including the observations from other bands [16][17][18][19][20].…”

Machine Learning Based Algorithms for Global Dust Aerosol Detection From Satellite Images: Inter-Comparisons and Evaluation

“…Even though these bands can provide sufficient information content for dust detection, it is hard to tell how much information is discarded by ignoring the observations from other bands. [10][11][12][13][14].…”