Volume XLII-3/W5 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W5, 61-66, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W5-61-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Oct 2018

29 Oct 2018

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

H. Shang1, H. Letu1,2, Z. Peng1, and Z. Wang1 H. Shang et al.
  • 1State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 20 Datun Road, Beijing 100101, China
  • 2Research and Information Center, Tokai University,2-28-4 Tomigaya, Shibuya-ku, Tokyo 151-0063, Japan

Keywords: Dust, Cloud mask, Aerosol loading detection, Himawari-8, CALIPSO

Abstract. 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 [R0.46μm, BT8.6μm/BT11.2μm, BTD(11.2μm–8.6μm) and BTD(12.4μm–11.2μm)] 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.