Review of dust storm detection algorithms for multispectral satellite sensors

Li, Jing; Wong, Man Sing; Lee, Kwon-Ho; Nichol, Janet Elizabeth; Chan, P. W.

doi:10.1016/j.atmosres.2020.105398

Cited by 21 publications

(12 citation statements)

References 75 publications

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“…Satellite images have many types related to the remote sensor technology that is used within each satellite. Some types are visible images, thermal infrared images, microwave images, and radar images (Li et al, 2021). Satellite images have three basic resolutions: spatial, spectral, and temporal.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Following the above, the approach of predicting dust storms is based on detecting dust particles from the image, and the researchers investigated the suitable bands that capture the dust, where each spectral band captures a different wavelength range (Li et al, 2021). However, this…”

Section: Data Sourcesmentioning

confidence: 99%

“…Since dust storm prediction is an urgent challenge in today's world, machine learning has opened various opportunities for studies in this field. A paper by Li et al (2021) reviewed a wide range of studies on dust storm detection using multispectral sensors. The paper covers many dust storm detection algorithms, such as those based on machine learning and empirical physical methods.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications to Dust Storms: A Meta-Analysis

Alshammari

Alrwais

Aksoy

2022

Aerosol Air Qual. Res.

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Dust storms are natural hazards that affect both people and properties. Therefore, it is important to mitigate their risks by implementing an early notification system. Different methods are used to predict dust storms, such as observing satellite images, analyzing meteorological data, and using numerical weather prediction model forecasts. However, recent studies have shown that machine learning algorithms have higher capacities to predict dust storms in less time and with fewer processing operations compared to numerical weather models. This paper conducted a meta-analysis review to examine studies that addressed the areas associated with the application of machine learning to dust storm prediction. It aims to compare the applied models and the types of data used in the literature under study. Given that the location of a dust storm event is essential, the properties of dust storms are discussed in relation to the region. The output classes and the various performance metrics observed in each reviewed paper are also summarized. Subsequently, the present paper offers a detailed analysis highlighting the capabilities of machine learning models in predicting dust storms. The analysis shows two main categories: early detection and dust storm prediction. Most models used for dust storm early detection from satellite images are support vector machines (SVM). In contrast, the most used models for dust storm prediction are SVM and random forests that predict the occurrence of dust storms from meteorological data. Finally, the paper highlights the challenges and future trends in the field, illustrating the potential directions for applying deep learning algorithms and providing longrange predictions with assessments of dust storm duration and intensity.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications to Dust Storms: A Meta-Analysis

Alshammari

Alrwais

Aksoy

2022

Aerosol Air Qual. Res.

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“…Due to the powerful learning capability of machine learning, it has shown high accuracy in regional dust monitoring. The machine learning approach, however, has some drawbacks for detecting dust such as difficulty for large area and near-real-time applications due to the computation requirements and easy over-fitting, especially in the presence of redundant features [37]. Compared to polar-orbiting satellites, the geostationary satellites have a higher frequency of observations, making them more suitable for dust detection [16,43].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data

et al. 2023

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Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

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“…Satellite remote sensing may be the only means of large-scale and all-day monitoring of dust events (Hu et al, 2008). While the duration of dust storms is from hours to days, and the coverage is substantial (Li et al, 2021), the ultra-high temporal sampling frequency of geostationary weather satellites makes them a better choice for dust detection.In the last decades, various detection methods have been developed based on the spectral properties of dust (Zhou et al, 2020). In the solar shortwave wavelengths, an important feature of dust is strong ultraviolet absorption.…”

mentioning

confidence: 99%

Dust Detection Over East Asia From Multispectral and Multi‐Temporal Himawari‐8/AHI Thermal Infrared Observations

Shi

Yang

Cui

et al. 2023

Earth and Space Science

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Dust storms are extremely destructive weather events that commonly occur over deserts worldwide and are transported to surrounding areas by strong winds (Park et al., 2014). The mineral dust aerosols affect the radiative energy budget and thermodynamic structure of the Earth and Atmosphere system through extinction and emission of radiation and play an essential role in processes governing the weather and climate system (Miller et al., 2017;Painter et al., 2010;Zheng et al., 2022). Dust storms are frequent in northwestern China and Mongolia every spring (Park et al., 2014;She et al., 2018). The dust can sweep most of northern and eastern China, South Korea, and Japan and cause severe air pollution and economic losses before settling in the ocean (Han et al., 2022). Surface observations are sparse, and difficult to locate and track dust transport. Satellite remote sensing may be the only means of large-scale and all-day monitoring of dust events (Hu et al., 2008). While the duration of dust storms is from hours to days, and the coverage is substantial (Li et al., 2021), the ultra-high temporal sampling frequency of geostationary weather satellites makes them a better choice for dust detection.In the last decades, various detection methods have been developed based on the spectral properties of dust (Zhou et al., 2020). In the solar shortwave wavelengths, an important feature of dust is strong ultraviolet absorption. The Absorption Aerosol Index (AAI) has been used to detect dust for more than 40 years based on the relatively small reflectance variation between the two ultraviolet channels in the presence of dust aerosols (Stein Zweers, 2021). Similar to AAI, the Dust Aerosol Index calculated from the reflectance of deep blue and blue channels is an effective metric for the preliminary separation of dust aerosols (Ciren & Kondragunta, 2014;Hsu et al., 2004;Zhou et al., 2020). The absorption of dust gradually decreases with increasing wavelength in the visible and near-infrared (VIR) wavelengths, leading it to appear generally as yellow on the true color image (Miller, 2003). Therefore, the reflectance, the reflectance ratio, and the reflectance normalized difference index of VIR bands can be used as a basic test for dust detection (

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Review of dust storm detection algorithms for multispectral satellite sensors

Cited by 21 publications

References 75 publications

Machine Learning Applications to Dust Storms: A Meta-Analysis

Machine Learning Applications to Dust Storms: A Meta-Analysis

Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data

Dust Detection Over East Asia From Multispectral and Multi‐Temporal Himawari‐8/AHI Thermal Infrared Observations

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