Dust detection and intensity estimation using Himawari-8/AHI observation.

• Published in: Remote Sensing
• Main Authors: She, Lu, Xue, Yong, Yang, Xihua, Guang, Jie, Li, Ying, Che, Yahui, Fan, Cheng, Xie, Yanqing
• Other Authors: University of Derby, Chinese Academy of Sciences, Beijing Normal University, New South Wales Office of Environment and Heritage
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2018
• Subjects: Big Data analytics; Aerosol Robotic Network
• Online Access: https://doi.org/10.3390/rs10040490; https://repository.derby.ac.uk/download/7001f74d9541d5642548ce5439f215a45c04adfa40efd4a774eed75327ebd0d6/36325741/Dust%20detection.pdf; https://repository.derby.ac.uk/download/c66e95f032df6c733aa37b1245896da7aee22f62703e8b2b89a4b9771103a03c/1689/license.txt

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.