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[1] Snow pit work of several-day intervals was performed simultaneously with radiation budget observations during two winters in eastern Hokkaido, Japan. From these data we investigated the effects of elapsed time after snowfall (snow aging), air temperature, snow surface temperature, snow grain size, and snow impurities on the visible and the near infrared albedos for improving the snow albedo scheme in the land surface process from an empirical model to a physically based model. The dependence of albedos on elapsed time after snowfall could be clearly classified by dividing the snow-covered period into a dry snow season and a wet snow season rather than by snow surface temperature. The albedo reduction by snow aging statistically depends on the snow surface temperature, which is often used to predict the snow albedo in the empirical model of land surface process. However, the albedo reduction rate was very scattered for snow surface temperatures above À10°C. This is because the snow albedo reduction essentially depends on the snow grain size and the concentration of snow impurities. Using the radiative transfer model for the atmosphere-snow system, the effects of these snow physical parameters on broadband albedos are calculated and compared with the observed ones. The measured broadband albedos fell close to the range of theoretically calculated ones as functions of these snow physical parameters. In particular, the measured near infrared albedo agreed well with the theoretically calculated ones both for the dependence of snow grain size and snow impurities but not as well for the visible albedo in detail. In the near infrared region the light absorption by ice is strong, and thus the snow albedo contains the information of snow physical parameters near the surface where these parameters are measured. In contrast, the visible albedo contains the snow information in the deeper layer because the ice is relatively transparent in the visible region. This suggests the necessity of the multiple-snow-layer model for the visible region in the physically based snow albedo model.

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The GCOM-C (SHIKISAI) satellite was developed to understand the mechanisms of global climate change. The Second-generation Global Imager (SGLI) onboard GCOM-C is an optical sensor observing wavelengths from 380 nm to 12.0 μm in 19 bands. One of the notable features is that the resolution of the 1.63-, 10.8-, and 12.0-µm bands is 250 m, with an observation frequency of 2-3 days. To investigate the effective use and potential of the 250-m resolution of these SGLI bands in the study of eruptive activities, we analyzed four practical cases. As an example of large-scale effusive activity, we studied the 2018 Kilauea eruption. By analyzing the series of 10.8-μm band images using cumulative thermal anomaly maps, we could observe that the lava effused on the lower East Rift Zone, initially owed down the southern slope to the sea, and then moved eastward. As an example of lava dome growth and generation of associated pyroclastic ows, the activity at Sheveluch between December 2018 and December 2019 was analyzed. The 1.63-and 10.8-µm bands were shown to be suitable for observing growth of the lava dome and occurrence of pyroclastic ows, respectively. We found that the pyroclastic ows occurred during periods of rapid lava dome expansion. For the study of an active crater lake, the activity of Ijen during 2019 was analyzed. The lake temperature was found to rise rapidly in mid-May and reach 38 °C in mid-June. We also analyzed the intermittent activities of small-scale Vulcanian eruptions at Sakurajima in 2019. The 1.63-µm band was useful for detecting activities that are associated with Vulcanian eruptions. Analytical results for these case studies demonstrated that the GCOM-C SGLI images are bene cial for observing various aspects of volcanic activity, and their real-time use may contribute to reducing eruption-related disasters.