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

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