[A-CG36] Terrestrial monitoring using new-generation geostationary satellites

[ACG36-08] Utilizing Himawari-8 Land Surface Temperatures to Detect Vegetation Drying

*Yuhei Yamamoto¹, Kazuhito Ichii¹, Youngryel Ryu², Minseok Kang³, Shohei Murayama⁴, Su-Jin Kim⁵ (1.Center for Environmental Remote Sensing, Chiba University, 2.Seoul National University, 3.National Center for AgroMeteorology, 4.National Institute of Advanced Industrial Science and Technology, 5.National Institute of Forest Science)

Keywords:Land surface temperature, Geostationary satellite, Himawari-8/AHI, Heat wave, Terrestrial vegetation

The land surface temperature (LST) varies depending on surface thermal inertia as well as solar radiation and atmospheric conditions. That is, a decrease in surface thermal inertia caused by drying leads to a higher variability in LST. In this study, we investigated the applicability of diurnal LST information on clear-sky days obtained from geostationary satellite Himawari-8 data to the detection of vegetation drying. The diurnal temperature cycle (DTC) parameters that summarize the diurnal cycle waveform were obtained by fitting a DTC model to the time-series LST information for each day. The DTC parameters include temperature-related parameters: T₀ (LST around sunrise), T_a (temperature rise during daytime), and δT (temperature fall during nighttime), T_max (daily maximum LST), T_min (daily minimum LST), and DTR (diurnal temperature range), along with time-related parameters: t_m (time reaching T_max), t_s (start time of nighttime cooling), and k (attenuation constant). The DTC parameters were compared with Visible Infrared Imaging Radiometer Suite (VIIRS) enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) data, Moderate Resolution Imaging Spectroradiometer (MODIS) latent heat flux (LE) data, and Soil Moisture Active Passive (SMAP) surface soil moisture (SM) data. We focused on the area around Japan and the Korean Peninsula, during summer in normal and heat wave conditions. For normal conditions, the spatial distribution of T_max exhibited the highest correlation with that of the vegetation indices, whereas the spatial distribution of DTR exhibited the highest correlation with that of the SM. During heat waves, the spatial distributions of DTR and T_min anomalies correlated with that of the SM. Furthermore, temporal correlation analysis showed a consistent negative correlation between DTR and SM. T₀, T_a, and δT also exhibited correlations with VIs, LE, and SM, however, they were more unstable than T_max, T_min, and DTR in the fitting. Time-related parameters did not correlate with any environmental factors. This study revealed that T_max and DTR obtained from the Himawari-8 LSTs are effective in detecting drying signals.