Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia

• DOI: 10.1016/j.scitotenv.2021.152462
• Publication Date: 2021-12-22T07:35:48Z
• Authors: Xue-Qing Ma; Pei Leng; Qian-Yu Liao; Yun-Jing Geng; Xia Zhang; Guo-Fei Shang; Xiaoning Song; Qian Song; Zhao-Liang Li
• Keywords: 2. Zero hunger; China; 13. Climate action; Climate Change; Temperature; 0207 environmental engineering; Seasons; 02 engineering and technology; 15. Life on land; Grassland; 01 natural sciences; 0105 earth and related environmental sciences

Vegetation phenology is a sensitive indicator of climate change and vegetation growth. In the present study, two phenological phases with respect to vegetation growth at the initial and mature stages, namely, the start of the season (SOS) and the peak of the season (POS), were estimated from a satellite-derived normalized difference vegetation index (NDVI) dataset over a long-term period of 32 years (1983 to 2014) and used to explore their responses to atmospheric variables, including air temperature, precipitation, solar radiation, wind speed and soil moisture. First, the forward feature selection method was used to determine whether each independent variable was linear or nonlinear to the SOS and POS. In addition, a generalized additive model (GAM) was used to analyze the correlation between the phenological phases and each independent variable at different temporal scales. The results show that soil moisture and precipitation are linearly correlated with the SOS, whereas the other variables are nonlinearly correlated. Meanwhile, soil moisture, wind speed and solar radiation are found to be nonlinearly correlated with the POS. However, air temperature and precipitation reveal a significant negative correlation with the POS. Furthermore, it was concluded that the aforementioned independent variables from the previous year could contribute to approximately 63%-85% of the SOS variations in the present year, whereas the atmospheric variables from April to June could contribute to approximately 70%-85% of the POS variations in the same year. Finally, the SOS and POS predicted by the GAM exhibit significant agreement with those derived from the satellite NDVI dataset, with the root mean square error of approximately 3 to 5 days.