Improved detection of low stratus and fog at dawn from dual geostationary (COMS and FY-2D) satellites

Jung Moon Yoo, Gyo Hwang Choo, Kwon Ho Lee, Dong L. Wu, Jung Hyun Yang, Jun Dong Park, Yong Sang Choi, Dong Bin Shin, Jin Hee Jeong, Jung Min Yoo

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


A novel method was proposed to detect low stratus and fog (LSF) at dawn during the summertime using near-simultaneous observations from dual geostationary-orbit satellites (GEOs): the Korean Communication, Ocean and Meteorological Satellite (COMS; 128.2°E) and the Chinese Feng-Yun-2D (FY-2D; 86.5°E). The orbital positions of the GEOs provided a large difference (~46.5°) in the viewing zenith angle (VZA) in the study region (122–132°E, 32.5–42.5°N) and high contrast observations at dawn. Numerical simulations were carried out to derive the optical properties of the LSF and the radiative differences between the GEOs due to both the VZA and spectral response function (SRF). The conventional threshold method, which used the visible reflectance at 0.67 μm (R0.67) and the brightness temperature difference between 3.7 μm and 11 μm (BTD3.7–11), had limitations in detecting LSF at dawn due to weak visible light and fast night-to-day transition. We utilized the observed stereo differences (ΔR0.67, ΔBTD3.7–11) between the two GEOs to improve LSF detection. The dual-satellite observations were verified with ground-based data from 45 stations in South Korea co-located with each GEO pixel. The ΔR0.67 threshold value showed better accuracy (78% vs. 67%) than the conventional R0.67 threshold method. In addition, the ΔBTD3.7–11 threshold was better (55% vs. 38%) than the BTD3.7–11 threshold. The dual-satellite method allowed more reliable LSF detection using the combination of ΔR0.67 and R0.67, particularly for LSF without cumuliform or high clouds. Our method is applicable to multiple geostationary satellites for continuous LSF monitoring.

Original languageEnglish
Pages (from-to)292-306
Number of pages15
JournalRemote Sensing of Environment
Publication statusPublished - 2018 Jun 15

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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