DEVELOPMENT OF A DAYTIME CLOUD AND AEROSOL LOADINGS DETECTION ALGORITHM FOR HIMAWARI-8 SATELLITE MEASUREMENTS OVER DESERT

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-3/W5; pp. 61 - 66
• Main Authors: Shang, H., Letu, H., Peng, Z., Wang, Z.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 29.10.2018; Copernicus Publications
• Subjects: Aerosols; Algorithms; Applied optics. Photonics; Atmospheric particulates; Brightness temperature; Clouds; Deserts; Detection; Distribution; Dust; Dust storms; Engineering (General). Civil engineering (General); Environmental monitoring; Fog; Haze; Pollution monitoring; Regions; Satellites; Sky; Storms; Surface radiation temperature; Synchronous satellites; T; TA1-2040; TA1501-1820; Technology; Wavelengths; China
• ISSN: 2194-9034; 1682-1750; 1682-1777; 2194-9034
• DOI: 10.5194/isprs-archives-xlii-3-w5-61-2018

Satellite cloud detection is essential for the downstream cloud and aerosol retrievals. However, the detection of clouds is easily biased by aerosol loadings (smoke, dust storm, haze etc.). Currently, Cloud mask products of satellites only provide the distribution of cloud and clear-sky areas. In the environmental monitoring applications in China, the distribution of haze pollution over the North China Plain and the dust plume generated from Taklimakan Desert are poorly identified from satellites. The next generation geostationary satellite Himawari-8 is equipped with the Advanced Himawari Imager (AHI), which can provide high temporal and spatial measurements in multiple wavelengths. In this study, a cloud and aerosol loading detection algorithm over China is proposed by improving our previous Himawari-8 cloud and haze mask (HCHM) algorithm to desert regions. The HCHM algorithm classifies the AHI pixels into one of three categories: clear, cloudy or aerosol loading. It should be noted that the aerosol loading regions include haze, fog or dust layers that are easily recognized by human eyes. Based on the brightness temperature sampling results of dust storm areas in infrared bands, the tests and their thresholds in distinguishing dust from cloud and clear-sky areas are determined. Several tests [R0.46 μm, BT8.6 μm/BT11.2μm, BTD(11.2 μm–8.6 μm) and BTD(12.4 μm–11.2 μm)] are used to detect the dust plume from deserts. Case study results indicate that our improved algorithm can provide reasonable distribution of dust storm, clouds and clear over desert regions.