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

文献类型： 外文期刊

第一作者： Ma, Xue-Qing

作者： Ma, Xue-Qing;Zhang, Xia;Shang, Guo-Fei;Li, Zhao-Liang;Leng, Pei;Geng, Yun-Jing;Song, Qian;Li, Zhao-Liang;Liao, Qian-Yu;Song, Xiaoning

作者机构：

关键词： Start of the season (SOS); Peak of the season (POS); Generalized additive model; Atmospheric reanalysis

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：10.753； 五年影响因子：10.237 ）

ISSN： 0048-9697

年卷期： 2022 年 812 卷

页码：

收录情况： SCI

摘要： 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.

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