Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation

Lu She,Yahui Che,Jie Guang,Yanqing Xie,Cheng Fan,Ying Li,Xihua Yang,Yong Xue

doi:10.3390/rs10040490

Abstract

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

Highlights

Mineral dust aerosol is tiny soil particle that is suspended in the atmosphere [1]
The dust detection and dust index algorithms were applied to Advanced Himawari Imager (AHI) data acquired from March to JuRneemionte 2Se0n1s7. 2.0D18u, 1s0t,dxeFtOeRctPioEnERreRsEuVlItEsWare qualitatively compared with the RGB images, and quant1i0taotfiv19ely compared with the dust detection results from the Aerosol Robotic Network (AERONET) measurements
We presented a dust detection method and a dust index to estimate the dust intensitFFyiiggouuvrreeer1111C

Summary

Introduction

Mineral dust aerosol is tiny soil particle that is suspended in the atmosphere [1]. It is one of the most important aerosols as it affects the Earth’s radiation balance, hydrological cycle, and biogeochemical cycles [1,2]. Dust particles can produce strong backscattering to solar radiation at shortwave band, which significantly changes the albedo of the Earth-atmosphere system. They can absorb incoming solar radiation and outgoing radiation, and alter the distribution of long-wave radiation [8,9]. Dust aerosol has a range of impacts on humans and environment [12,13]

Methods

Results

Conclusion