山本 雄平

Recent advancements in new generation geostationary satellites have facilitated the application of their datasets to terrestrial monitoring. In this application, geolocation accuracy is an essential issue because land surfaces are generally heterogeneous. In the case of the Advanced Himawari Imager (AHI) onboard Himawari-8, geometric correction of the Himawari Standard Data provided by the Japan Meteorological Agency (JMA data) was conducted using thermal infrared band with 2km spatial resolution. Based on JMA data, the Center for Environmental Remote Sensing (CEReS) at Chiba University applied a further geometric correction using a visible band with 500m spatial resolution and released a dataset (CEReS data). JMA data target more general users mainly for meteorological observations, whereas CEReS data aim at terrestrial monitoring for more precise geolocation accuracy. The objectives of this study are to clarify the temporal and spatial variations of geolocation errors in these two datasets and assess their stability for unexpected large misalignment. In this study, the temporal tendencies of the relative geolocation difference between the two datasets were analyzed, and temporal fluctuations of band 3 reflectances of JMA data and CEReS data at certain fixed sites were investigated. A change in the geolocation trend and occasional shifts greater than 2 pixels were found in JMA data. With improved image navigation performance, the geolocation difference was decreased in CEReS data, suggesting the high temporal stability of CEReS data. Overall, JMA data showed an accuracy of less than 2 pixels with the spatial resolution of band 3. When large geolocation differences were observed, anomalies were also detected in the reflectance of JMA data. Nevertheless, CEReS data successfully corrected the anomalous errors and achieved higher geolocation accuracy in general. As CEReS data are processed during the daytime due to the availability of visible bands, we suggest the use of CEReS data for effective terrestrial monitoring during the daytime.

Understanding the spatiotemporal characteristics of land surface temperature (LST), a physical quantity that greatly contributes to the formation of urban thermal environment, is essential for constructing a sustainable urban environment. This study explores the relationship between urban space, atmospheric conditions, and diurnal change characteristics of LST on summer clear-sky days in an urban area of Osaka Plain using a 10 min LST dataset retrieved from the Japanese geostationary satellite (Himawari-8) data. The principal temporal change patterns forming the diurnal cycle of LST were extracted through a principal component analysis, and their spatial variabilities were investigated through the exploitation of ground-based meteorological observation and land-use data. The result showed two temporal change patterns, identified as the amplitude and phase of the diurnal change, being extracted in different clear-sky days. A hierarchical cluster analysis confirmed that the spatial variations of such patterns corresponded to the sea breeze and urban land-use. Moreover, LST change at daytime was found to be larger in the high-density areas rather than in the low-density areas of low buildings, thereby suggesting the influence of building density on LST at daytime.

This paper provides the first attempt to investigate the spatial variability of diurnal change patterns of land surface temperature (LST) in urban areas of Japan by applying principal component analysis on LST data retrieved from Himawari-8 geostationary satellite data. The Tokyo and Osaka metropolitan areas were the focus of the analysis, and the target days were days with zero cloud cover in summer and winter. The results of the analysis showed that diurnal cycles of LST are mainly formed by two temporal change patterns in both seasons. For the summer case, the first two principal components (PCs) represented the temporal change patterns related to the amplitude and phase, respectively. For the winter case, the first two PCs represented the temporal change patterns related to the amplitude and gradual change in LST throughout the day, respectively. Results suggest that these temporal change patterns in both seasons have spatial variability partially dictated by land use and wind speed/direction.