VALIDATION OF ASMR2 SEA ICE CONCENTRATION DATA USING MODIS DATA

• Published in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Main Authors: Cho, K., Nagao, R., Naoki, K.
• Format: Text
• Language: English
• Published: 2019
• Subjects: Sea ice
• Online Access: https://doi.org/10.5194/isprs-archives-XLII-2-W13-1741-2019; https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1741/2019/

Passive microwave radiometer AMSR2 was launched by JAXA in May 2012 on-board GCOM-W satellite. The antenna diameter of AMSR2 is 2.0 m which provide highest spatial resolution as a passive microwave radiometer in space. The sea ice concentration images derived from AMSR2 data allow us to monitor the detailed sea ice distributions of whole globe every day. The AMSR bootstrap algorithm developed by Dr. Josefino Comiso is used as the standard algorithm for calculating sea ice concentration from AMSR2 data. Under the contract with JAXA, the authors have been evaluating the performance of the algorithm. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W. Since the spatial resolution of MODIS is much higher than that of AMSR2, under the cloud free condition, the ice concentration corresponds to the size of a pixel of AMSR2 can be calculated much accurately with MODIS data. The procedures of the evaluation are as follows. Firstly, MODIS band 1 reflectance were binarized to discriminate sea ice(1) from open water(0) and sea ice concentration of each pixel size of AMSR2 were calculated. In calculating sea ice concentration from MODIS data, the selection of the threshold level of MODIS band 1 reflectance is critical. Through the detailed evaluation, the authors selected 5% as the optimum threshold level. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10% error under the cloud free condition.