Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia
文献类型：期刊论文
| 作者 | Ma, Xue-Qing2; Leng, Pei3; Liao, Qian-Yu1; Geng, Yun-Jing3; Zhang, Xia2; Shang, Guo-Fei2; Song, Xiaoning4; Song, Qian3; Li, Zhao-Liang2,3 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2022-03-15 |
| 卷号 | 812页码:12 |
| ISSN号 | 0048-9697 |
| 关键词 | Start of the season (SOS) Peak of the season (POS) Generalized additive model Atmospheric reanalysis |
| DOI | 10.1016/j.scitotenv.2021.152462 |
| 通讯作者 | Leng, Pei(lengpei@caas.cn) |
| 英文摘要 | 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. |
| WOS关键词 | AKAIKES INFORMATION CRITERION ; CLIMATE-CHANGE ; NORTHERN-HEMISPHERE ; TIME-SERIES ; NDVI ; MODEL ; VALIDATION ; RESPONSES ; INDEX |
| 资助项目 | National Natural Science Foundation of China[41921001] ; National Natural Science Foundation of China[42041005] ; National Natural Science Foundation of China[41801371] ; Central Public-interest Scientific Institution Basal Research Fund[Y2020YJ07] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000740223300007 |
| 资助机构 | National Natural Science Foundation of China ; Central Public-interest Scientific Institution Basal Research Fund |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169716]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Leng, Pei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Hebei GEO Univ, Sch Land Sci & Spatial Planning, Shijiazhuang 050031, Hebei, Peoples R China 3.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Xue-Qing,Leng, Pei,Liao, Qian-Yu,et al. Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,812:12. |
| APA | Ma, Xue-Qing.,Leng, Pei.,Liao, Qian-Yu.,Geng, Yun-Jing.,Zhang, Xia.,...&Li, Zhao-Liang.(2022).Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia.SCIENCE OF THE TOTAL ENVIRONMENT,812,12. |
| MLA | Ma, Xue-Qing,et al."Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia".SCIENCE OF THE TOTAL ENVIRONMENT 812(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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