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Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where in situ time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with in situ spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture in situ-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

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Synergistic applications of multi-resolution satellite data have been of a great interest among user communities for the development of an improved and more effective operational monitoring system of natural resources, including vegetation and soil. In this study, we conducted an inter-comparison of two remote sensing products, namely, visible/near-infrared surface reflectances and spectral vegetation indices (VIs), from the high resolution Advanced Thermal Emission and Reflection Radiometer (ASTER) (15 m) and lower resolution Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m – 500 m) sensors onboard the Terra platform. Our analysis was aimed at understanding the degree of radiometric compatibility between the two sensors' products due to sensor spectral bandpasses and product generation algorithms. Multiple pairs of ASTER and MODIS standard surface reflectance products were obtained at randomly-selected, globally-distributed locations, from which two types of VIs were computed: the normalized difference vegetation index and the enhanced vegetation indices with and without a blue band. Our results showed that these surface reflectance products and the derived VIs compared well between the two sensors at a global scale, but subject to systematic differences, of which magnitudes varied among scene pairs. An independent assessment of the accuracy of ASTER and MODIS standard products, in which “in-house” surface reflectances were obtained using in situ Aeronet atmospheric data for comparison, suggested that the performance of the ASTER atmospheric correction algorithm may be variable, reducing overall quality of its standard reflectance product. Atmospheric aerosols, which were not corrected for in the ASTER algorithm, were found not to impact the quality of the derived reflectances. Further investigation is needed to identify the sources of inconsistent atmospheric correction results associated with the ASTER algorithm, including additional quality assessments of the ASTER and MODIS products with other atmospheric radiative transfer codes.

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