[H-TT15] Environmental Remote Sensing

[HTT15-P04] Investigating the Interactions Among Phenological Events of Vegetation Based on Field Measurements and Long-term Satellite Observations

*楊 偉¹ (1.千葉大学)

キーワード：リモートセンシング、フェロノジー、生物的ファクター、気候変動、北半球

Identifying complete drivers for vegetation phenology changes is crucial for improving phenology prediction models. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of different vegetation types, including forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, and whether changes in autumn phenology facilitate earlier green-up date of northern vegetation. In this study, we first investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP); then, we turned to focus on autumn phenology, a critical biotic factor that is likely to affect the subsequent spring phenology of vegetation. The relationships among the green-up date (GUD), heading date (HD), and maturity date (MD) were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). On the other hand, we examined the association between the start and end of growing season (SOS and EOS) in the following year in northern middle and high latitudes (north of 25°N). Interannual changes in SOS were significantly (P < 0.05) related to changes in EOS in the previous year in 26.4% of the total pixels, mostly in the boreal region, with a 1-day advance of EOS generally resulting in about a 0.5- to 1.0-day advance of the following SOS, suggesting that the advanced SOS may be associated with the advanced EOS. These results suggest that the phenological interactions should be included in the future development of vegetation phenology models to improve the prediction accuracies.