Evaluating the potential of H8/AHI geostationary observations for monitoring vegetation phenology over different ecosystem types in northern China
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- International Journal of Applied Earth Observation and Geoinformation, 2022, 112
- Issue Date:
- 2022-08-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S1569843222001315-main.pdf | 8.66 MB |
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Sensors onboard the new generation of geostationary (GEO) satellites launched over past few years have much improved radiometric and temporal resolutions. As one of the latest GEO sensors, the Japanese Himawari-8 Advanced Himawari Imager (hereafter H8/AHI) has temporal resolution of 10 minutes and spectral bands similar to MODIS. Observations from H8/AHI therefore have the potential to improve the precision of vegetation phenology monitoring by significantly shortening the compositing period. In this study, we evaluated methods for compositing daily 2-band Enhanced Vegetation Index (EVI2) from 10-minutes H8/AHI observations at six study sites in northern China encompassing forest, cropland, and grassland. After screening the cloudy observations by a physics-based cloud detection algorithm, key phenological metrics were retrieved from the composited AHI EVI2. H8/AHI 10-min raw EVI2 showed significant diurnal variations that are mainly associated with sun angle variations. By calculating the daily average EVI2 values in different time intervals, we concluded that the mean value using a fixed time window (10:30–13:30) centered around local solar noon effectively reduced the anisotropy effect from the diurnal sun-angle variations, leading to stable EVI2 time series suitable for extracting phenological metrics. On average, the amount of the composited H8/AHI EVI2 time-series data was 5.3 times more than the Terra/Aqua combined MODIS EVI2 product per year and the average size (width) of the gaps in H8/AHI EVI2 time series was 75% smaller than that of the MODIS. Our results demonstrated the promising capability of the new generation GEO satellite for generating time series vegetation index with fewer cloud-induced gaps and higher temporal resolution, hence improving the monitoring of global vegetation phenology and ecosystem responses to climate change.
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