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Phenology is closely related to carbon, water and other material cyclings on the earth's surface, and one of the most important indicators to understand vegetation responses to climate change. Satellite data provides us with a tool to monitor and assesses spatio-temporal variations in phenology. However, phenology based on satellite observation does not validated extensively with green-up or leaves dropping date acquired by ground based observation because of the lack in ground truth data for validation. By the way, Japanese Meteorological Agency (JMA) have observed the timing of flowering, budding, leaves coloring and dropping at more than 100 meteorological stations in Japan since 1953. In this study, green-up date detected from NOAA AVHRR observation was validated with JMA's phenological data, and four algorithms to detect green-up date were evaluated. As a result, spatio-temporal variation in green-up dates detected by newly developed algorithm was highly consistent with the ground data compared to another algorithms, and RMSE between the retrieved green-up dates and in situ data was 6.6 days. And, it was found that the algorithm was able to detect responses of green-up date to changes in annual temperature.

A new NOAA AVHRR-based phenology detection method over deciduous forests in northeastern Asia