9:30 AM - 9:45 AM
[ACG38-03] Utility of the meteorological satellite Himawari-8 for planetary science: lunar surficial physical properties implied from the infrared spectrum
Keywords:Meteorological satellite, Himawari-8, Planetary science, Moon, Infrared spectrum, Image analysis
The Japanese meteorological satellite Himawari-8 is known to extend its scan range slightly outside the Earth and capture the Moon and other solar-system objects. Owing to the high spatio-temporal resolution of the onboard Advanced Himawari Imager (AHI), for example, the lunar images with a resolution as good as 23 km/pix have been taken more than 900 times in the infrared bands since the beginning of its operation in 2015. It is noteworthy that the wavelengths of nine infrared bands of AHI have not been covered in previous spaceborne lunar observations; AHI may have great potential for new lunar science and calibration of interplanetary spacecraft. Nevertheless, the Moon captured in AHI images has never been recognized in planetary science. Therefore, to explore the possibility of utilization of Himawari-8, we investigate the lunar surficial environment using AHI infrared data.
In this study, we extract lunar images from all the Himawari Standard Data (HSD) taken by the end of November 2021, linking all pixels to the lunar longitude and latitude. After evaluating the background noise of the lunar images, we calculate brightness temperatures on the lunar surface from the radiance values. Then, we first compare our results with the measurements by the Diviner radiometer onboard NASA's Lunar Reconnaissance Orbiter. The brightness temperatures in band 11 (8.40 – 8.78 μm) of AHI and channel 5 (8.38 – 8.68 μm) of Diviner [1] show a good agreement. Although the count around noontime is saturated, the equatorial brightness temperatures of HSD in the morning and evening match those of Diviner within a standard deviation. The temperature curves during the nighttime are also obtained by stacking HSDs. They are also consistent with the Diviner data in high-temperature regions such as Tycho crater, indicating that HSD is useful for cross-calibration for other spaceborne instruments.
Furthermore, we estimate the lunar surface roughness and rock abundance by comparing the observations and simulations. HSD clearly shows that the estimated brightness temperatures depend on the wavelength. This is caused by the non-linear mixing of thermal radiation from various temperatures within a single pixel. In the morning and evening sides, this phenomenon is caused by the large variation in solar incident angles on rough terrains. From previous lunar missions, the lunar roughness is known to have various mm-scale slopes, causing temperature differences between west- and east-facing surfaces [2]. Our simulations with the average roughness angle of the Apollo landing sites agree with the HSD within 10 %. On the other hand, the night temperatures can be interpreted as the mixing effect of rock and sand. Due to the difference in thermal inertia, sand cools more rapidly than rocks, and the temperature difference can be as high as 100 K at midnight. Based on the comparison between the rock-sand mixing model simulations and the observations, the rock concentrations are estimated as 0.18 – 0.48 and 5.6 – 10.4 % at the equator (10°N – 10°S) and Tycho crater, respectively. These estimates are consistent with the previous lunar constraints from other missions [3].
These results show that HSD potentially serves as a new dataset for lunar and planetary sciences. We will discuss the further utility of the AHI for planetary science in this presentation.
[1] Paige D. A. et al. (2010) Space Sci. Rev., 150, 125-160.
[2] Bandfield J. L. et al. (2015) Icarus, 248, 357-372.
[3] Bandfield J. L. et al. (2011) J. Geophys. Res: Planets, 116, E00H02
In this study, we extract lunar images from all the Himawari Standard Data (HSD) taken by the end of November 2021, linking all pixels to the lunar longitude and latitude. After evaluating the background noise of the lunar images, we calculate brightness temperatures on the lunar surface from the radiance values. Then, we first compare our results with the measurements by the Diviner radiometer onboard NASA's Lunar Reconnaissance Orbiter. The brightness temperatures in band 11 (8.40 – 8.78 μm) of AHI and channel 5 (8.38 – 8.68 μm) of Diviner [1] show a good agreement. Although the count around noontime is saturated, the equatorial brightness temperatures of HSD in the morning and evening match those of Diviner within a standard deviation. The temperature curves during the nighttime are also obtained by stacking HSDs. They are also consistent with the Diviner data in high-temperature regions such as Tycho crater, indicating that HSD is useful for cross-calibration for other spaceborne instruments.
Furthermore, we estimate the lunar surface roughness and rock abundance by comparing the observations and simulations. HSD clearly shows that the estimated brightness temperatures depend on the wavelength. This is caused by the non-linear mixing of thermal radiation from various temperatures within a single pixel. In the morning and evening sides, this phenomenon is caused by the large variation in solar incident angles on rough terrains. From previous lunar missions, the lunar roughness is known to have various mm-scale slopes, causing temperature differences between west- and east-facing surfaces [2]. Our simulations with the average roughness angle of the Apollo landing sites agree with the HSD within 10 %. On the other hand, the night temperatures can be interpreted as the mixing effect of rock and sand. Due to the difference in thermal inertia, sand cools more rapidly than rocks, and the temperature difference can be as high as 100 K at midnight. Based on the comparison between the rock-sand mixing model simulations and the observations, the rock concentrations are estimated as 0.18 – 0.48 and 5.6 – 10.4 % at the equator (10°N – 10°S) and Tycho crater, respectively. These estimates are consistent with the previous lunar constraints from other missions [3].
These results show that HSD potentially serves as a new dataset for lunar and planetary sciences. We will discuss the further utility of the AHI for planetary science in this presentation.
[1] Paige D. A. et al. (2010) Space Sci. Rev., 150, 125-160.
[2] Bandfield J. L. et al. (2015) Icarus, 248, 357-372.
[3] Bandfield J. L. et al. (2011) J. Geophys. Res: Planets, 116, E00H02