# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 1 Introduction  
Dust storm is a natural disaster that causes enormous damage to the environment, transportation, and crops [1]. It also has direct and indirect impacts on public health and human health [2; 3]. Small volumes of dust particles can enter the respiratory tract and lungs, causing asthma, pneumonia, and other respiratory and cardiovascular diseases [4; 5]. Studies have shown that exposure to dust particles in the air for hours to days can lead to other problems such as conjunctivitis, meningitis, and valley fever [6; 7; 8; 9]. There are currently four major dust source areas and dust-prone zones in the world, located in northern Africa, eastern Asia, North America, and Australia [10]. The Taklamakan and Gobi Deserts in northwestern China are the main dust source in East Asia and also the second largest dust source in the world [10]. Dust storm episodes typically occur in the spring (March-May) [11; 12] and cause great damage to the economy and people's lives. Therefore, there are economic, social, and scientific benefits from monitoring dust storms and understanding the transport, spatial, and temporal distribution of dust [13; 14]. Dust monitoring can be achieved by ground-based observations and satellite remote sensing [15; 16]. Data from ground-based air quality monitoring stations are often used to study dust events [17]. However, due to the limited number of ground stations, the dynamic monitoring of dust transport is challenging based on ground-based observations. Dust mainly occurs in desert areas where meteorological stations are not easy to set up and maintain [18]. With the development of meteorological satellite technology, remote sensing images with a high observation frequency and wide spatial coverage play an essential role in dust detection and monitoring [19]. Satellite remote sensing can not only determine the dust source, transport path, impact range, and intensity of dust events but also monitor the dynamics of dust formation, development, and spread [20]. Theoretically, airborne dust can be detected with different optical properties in the ultraviolet (UV), visible (VIS), and infrared (IR) satellite images [21]. Dust detection methods can be generally divided into three categories depending on the channels used. The first category is based on the absorbability of dust aerosols at shorter wavelengths [22]. Typical examples are the absorbing aerosol index from Ozone Monitoring Instrument (OMI) [22; 23] and the normalized difference dust index (NDDI) proposed by [PERSON] et al. [24]. Second, methods based on the brightness temperature (BT) in thermal infrared (TIR) channels are more widely used due to the capability to detect dust over bright surfaces and at night [25; 26]. Among them, the brightness temperature difference (BTD) between two IR channels [27; 28] and infrared difference dust index (IDDI) [29] are the two classic dust detection methods. According to [PERSON] [27], the BTD between 8.5, 11, and 12 \(\upmu\)m of Moderate Resolution Imaging Spectroradiometer (MODIS) data is capable of detecting dust aerosols. Negative BTD between 11 \(\upmu\)m and 12 \(\upmu\)m (BTD (11-12 \(\upmu\)m)) typically indicate dust presence. IDDI is more often applied to geostationy satellites and has also been used in the operational dust monitoring system in China [30]. Based on the above two types of methods, the combination of shortwave reflectance and infrared brightness temperatures to detect dust have also been developed [31; 32; 33]. Table 1 lists several representative indices for dust detection based on the reflectivity and emissivity of airborne dust at multiple channels. However, the ability of these methods are influenced by various factors such as sensor spectral channel configurations and the target area's surface reflection and thermal characteristics. In recent years, machine learning has been increasingly used to detect dust with satellite data [34; 35; 36]. Due to the powerful learning capability of machine learning, it has shown high accuracy in regional dust monitoring.