# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 1 Introduction
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]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel Type** & **Dust Index** & **Algorithm** & **Reference** \\ \hline \multirow{2}{*}{VIS and NIR} & NDDI & \((\text{R}_{0.499}-\text{R}_{2.13})/(\text{R}_{0.499}-\text{R}_{2.10})\) & [PERSON] et al. (2006) [24] \\ \cline{2-4}  & Dust aerosol index (DAI) & \(\text{DAI}=-100\text{[log}(\text{R}_{120m}-\text{R}_{480m})-10\text{[log}( \text{R}_{480m}-\text{R}_{210m})]\) & [PERSON] et al. (2014) [38] \\ \cline{2-4}  & & Nondust absorbing aerosol index (DAI) & \(\text{TE}_{0.1}-10\text{[log}(\text{R}_{120m}-\text{R}_{210m})]\) & [PERSON] (1997) [28] \\ \hline \multirow{6}{*}{TIR} & BTD & BTD (3.7–11 \(\upmu\)m) & [PERSON] (1989) [27] \\ \cline{2-4}  & BTD (8.5–11 \(\upmu\)m) & [PERSON] (1997) [28] \\ \cline{2-4}  & IDDI & \(\text{BT}_{1}-\text{BT}_{2}\), where i represents the real-time target brightness temperature, \(j\) represents the background brightness temperature & [PERSON] et al. (2001) [29] \\ \cline{2-4}  & Thermal infrared dust index & \(\text{C}_{0}+\text{C}_{1}\times\text{BT}_{3.7}+\text{C}_{2}\times\text{BT}_{ 3.7}+\text{C}_{3}\times\text{RT}_{11}+\text{C}_{4}\times\text{BT}_{12}\) & [PERSON] and [PERSON] (2007) [39] \\ \cline{2-4}  & & Middle East dust index (MED) & \((\text{BT}_{11}-\text{BT}_{3.5})/(\text{BT}_{12}-\text{BT}_{3.5})\) & [PERSON] et al. (2012) [40] \\ \cline{2-4}  & Brightness temperature adjusted & \(2/\text{T}\text{1}\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \textCompared to polar-orbiting satellites, the geostationary satellites have a higher frequency of observations, making them more suitable for dust detection [16; 43]. Geostationary satellites used to study dust monitoring for China include the Multifunctional Transport Satellite (MTSAT-2) and Himawari 8 launched by Japan [12] and Feng Yun-2 (FY-2) [30] and Feng Yun-4 (FY-4) [44] launched by China. Himawari-8 and FY-4A, as the new generation of geostationary satellites, are superior to MTSAT-2 and FY-2 in terms of temporal resolution, spatial resolution, and the number of measurement channels. Compared to Himawari-8, FY-4A is more appropriate for dust storm monitoring over China, with a broad observation range covering the entirety of China. [PERSON] et al. [44] conducted FY-4A-based dust monitoring based on machine learning and found that machine learning has the advantage in identifying dust over specific areas. [PERSON] et al. [45] analyzed three dust events that occurred in 2021 with FY-4A data. Additionally, the China Meteorological Administration (CMA) developed two dust products based on FY-4A data including the dust score product (DST) and dust intensity product (IDDI_BK). [PERSON] et al. [46] found that these two FY-4A L2 dust detection products in the northwest region agreed well with the MODIS AOD products. All of these studies demonstrate that FY-4A has good potential for dust detection. So far, few studies have comprehensively and quantitatively evaluated the performance of different dust detection methods based on FY-4A for dust detection over China. Therefore, this study aimed to evaluate the ability of the BTD, NDDI, CMA IDDI, and DST algorithms/products to detect dust. Focusing on dust detection over China based on FY-4A data, this study:  
1. Qualitatively evaluated the performance of the four algorithms/products on the dust identification during two typical dust events;
2. Quantitatively compared the BTD, NDDI, and two FY-4A dust products with the real-time ground-based PM10 (less than 10\(\upmu\)m in aerodynamic diameter) concentration data and assess their accuracy in identifying dust in the spring of 2021.