11:00 〜 13:00
[ACG38-P05] GOES-R Advanced Baseline Imager による砂漠植生フェノロジー観測
キーワード：GOES-R、正規化植生指標、植生フェノロジー
The Geostationary Operational Environmental Satellite – R Series (GOES-R) is one of new-generation geostationary satellites. Currently, two GOES-R satellites, GOES-16 and GOES-17, are in orbit, acquiring a variety of data for weather monitoring, science, and forecasting. The Advanced Baseline Imager (ABI) onboard each GOES-R satellite is equipped with a red and near-infrared bands suitable for monitoring vegetation photosynthetic activities. Each ABI images over the conterminous US at 5 min intervals. This new-generation geostationary satellite sensor has the potential to generate higher-temporal resolution vegetation index (VI) time series data than the conventional polar-orbiting satellite sensors in seasonally heavily clouded areas. Vegetation phenology of the Sonoran desert in the Southwest US is influenced by the seasonal rainfall pattern. In other words, the start and length of the vegetation growing season (SOS and LOS, respectively) is controlled mainly by the timing and amount of the monsoon rainfall. In our previous analysis of vegetation phenology in this area, conventional polar-orbiting satellite sensor (i.e., Moderate Resolution Imaging Spectroradiometer) data had a large data gap around the monsoon season, resulting in large uncertainties in the satellite-derived SOS and LOS. In this study, we assessed how the hypertemporal data from ABI could improve the SOS and LOS estimation relative to a conventional satellite sensor, Visible Infrared Imaging Spectroradiometer (VIIRS), in Arizona, USA. The temporal signatures of the normalized difference vegetation index (NDVI) derived from ABI and VIIRS were comparable. The latter data were, however, subject to more frequent data gaps. Both datasets were subject to variations associated with varying sun-target-view geometries to the same extent. The ABI NDVI temporal signatures were, however, subject to smaller uncertainties than the VIIRS NDVI for the former having a much larger number of observations. These results indicate that the ABI hypertemporal data are advantageous even for reducing the uncertainty in characterizing desert vegetation phenology.