Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China
文献类型：期刊论文
| 作者 | Yuan, Huanhuan1,2; Wang, Xiaoyue1; Wu, Chaoyang1,2; Wang, Huanjiong1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2020 |
| 卷号 | 13页码:717-725 |
| 关键词 | Gross primary productivity (GPP) leaf coloring date (LCD) nighttime temperature phenology vegetation index |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2020.2971098 |
| 通讯作者 | Wang, Xiaoyue(wangxy@igsnrr.ac.cn) |
| 英文摘要 | Plant phenology is of great significance for global change study and it serves as an important indicator of vegetation productivity. Increasing efforts have been made to retrieve the plant phenology using remote sensing observations at regional-to-global scale due to its large spatial coverage. Compared with our understanding on drivers of spring phenology, it remains unclear that to which extent is leaf coloration in autumn controlled by climate forcing, especially on the relative importance between daytime and nighttime temperatures. Using a total of 160 site-year leaf coloring date (LCD) data observed from 14 sites in China, we showed that three frequently used remote sensing algorithms (i.e., the dynamic threshold approach, the simple and double logistic approaches) were not able to accurately retrieve LCD. Surprisingly, the nighttime land surface temperature (LSTnight) explained as much as 62% of LCD variability, compared with 28% for daytime temperature (LSTday). We, therefore proposed a new model that combines the enhanced vegetation index and LSTnight for the reconstruction of LCD. We demonstrated that LCD of China's ecosystems has been delayed at a rate of 0.7 days per year over 2003-2014, and a longer LCD contributed to the increased annual gross primary productivity for most (66%) regions. Our results have important implications as it sheds light on the role of LSTnight in controlling plant phenology. This article strongly suggests the combined use of vegetation index and LSTnight in the reconstruction of phenological variations in autumn across species and plant functional types. |
| WOS关键词 | GROSS PRIMARY PRODUCTION ; GROWING-SEASON LENGTH ; VEGETATION PHENOLOGY ; SPRING PHENOLOGY ; INTERANNUAL VARIABILITY ; DECIDUOUS FOREST ; CLIMATE-CHANGE ; AUTUMN ; NDVI ; DAYTIME |
| 资助项目 | National Natural Science Foundation of China[41871255] ; National Natural Science Foundation of China[41901359] ; Key Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000526773100006 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/133856]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Xiaoyue |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuan, Huanhuan,Wang, Xiaoyue,Wu, Chaoyang,et al. Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2020,13:717-725. |
| APA | Yuan, Huanhuan,Wang, Xiaoyue,Wu, Chaoyang,&Wang, Huanjiong.(2020).Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,717-725. |
| MLA | Yuan, Huanhuan,et al."Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):717-725. |
入库方式： OAI收割
来源：地理科学与资源研究所
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