Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 485-490, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-485-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 485-490, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-485-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING MODIS DATA

K. Hayashi, K. Naoki, and K. Cho K. Hayashi et al.
  • Tokai University, 2-28-4, Tomigaya, Shibuya-ku, Tokyo, Japan

Keywords: Sea Ice, Optical Sensor, Global Warming, Glaciology

Abstract. Sea ice has an important role of reflecting the solar radiation back into space. However, once the sea ice area melts, the area starts to absorb the solar radiation which accelerates the global warming. This means that the trend of global warming is likely to be enhanced in sea ice areas. In this study, the authors have developed a method to extract thin ice area using reflectance data of MODIS onboard Terra and Aqua satellites of NASA. The reflectance of thin sea ice in the visible region is rather low. Moreover, since the surface of thin sea ice is likely to be wet, the reflectance of thin sea ice in the near infrared region is much lower than that of visible region. Considering these characteristics, the authors have developed a method to extract thin sea ice areas by using the reflectance data of MODIS (NASA MYD09 product, 2017) derived from MODIS L1B. By using the scatter plots of the reflectance of Band 1 (620 nm–670 nm) and Band 2 (841 nm–876 nm)) of MODIS, equations for extracting thin ice area were derived. By using those equations, most of the thin ice areas which could be recognized from MODIS images were well extracted in the seasonal sea ice zones in the Northern Hemisphere, namely the Sea of Okhotsk, the Bering Sea and the Gulf of Saint Lawrence. For some limited areas, Landsat-8 OLI images were also used for validation.