Abstract

The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies.

Highlights

  • Geostationary satellite data have been used for meteorological and ocean applications for many decades

  • The color coded profiles reveal the diurnal range of solar zenith angles experienced and show significant Advanced Himawari Imager (AHI) vegetation index (VI) variations caused by sun angle related BRDF influences throughout the day (Figure 7)

  • Higher normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)/ EVI2 values were found at the larger solar zenith angles encountered at the beginning and end of each day, with the lowest VI values occurring at the smallest solar zenith angles near local solar noon

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Summary

Introduction

Geostationary satellite data have been used for meteorological and ocean applications for many decades. The improved spectral and spatial resolutions render these new generation geostationary (GEO) sensors comparable to low earth orbiting (LEO) satellites, such as Terra/Aqua–Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi Visible Infrared Imaging Radiometer Suite (VIIRS), but with the advantage of much finer temporal resolution. There have been few cross-sensor vegetation index (VI) studies investigating the degree to which the fine temporal resolution capabilities of GEO satellites are compatible with and can augment time series data from LEO sensors, such as MODIS and VIIRS. MODIS/ VIIRS vegetation index (VI) time series data and VI-based phenology profiles could potentially be gap-filled with AHI data to enhance their temporal fidelity. A part of these variations may have been caused by atmospheric influences in the AHI top-of-atmosphere (TOA) reflectances relative to the atmosphere-corrected MODIS surface reflectances [8,18,19]

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