A new algorithm for the estimation of leaf unfolding date using MODIS data over China's terrestrial ecosystems
文献类型：期刊论文
| 作者 | Wang, Jian2,3; Wu, Chaoyang1,3; Wang, Xiaoyue1; Zhang, Xiaoyang4 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2019-03-01 |
| 卷号 | 149页码:77-90 |
| 关键词 | Remote sensing Spring phenology EVI2 Preseason temperature PFT |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2019.01.017 |
| 英文摘要 | Using solely vegetation indices (VIs) from remote sensing is not always sufficient to accurately detect spring leaf phenology, i.e., the leaf unfolding date (LUD). Several current phenology products failed to provide reliable LUD estimates for specific regions and plant functional types, e.g., evergreen species at mid-low latitudes. Therefore, increasing efforts have been made to improve LUD modeling by combining VIs and meteorological variables. Temperature before the growing season ('preseason' henceforth) plays an important role in regulating spring phenology. With ground observations of LUD (LUDOBS) across different plant functional types (PFTs) in China during 2001-2014, we analyzed the response of LUDOBS to preseason temperature temporally and spatially, and proposed an improved LUD modeling algorithm by developing a temperature-based scale factor to adjust the traditional VI-based (i.e., two band enhanced vegetation index (EVI2)) LUD estimates. We found that the new algorithm can better characterize the spatial and temporal patterns of LUD variability for different PFTs, especially for evergreen species where MODIS phenology product failed to provide reliable LUD estimates. Furthermore, we investigated the spatio-temporal patterns of LUD over China with respect to both different vegetation types and climate systems. We showed that for similar to 70% pixels, our new model predicted an overall later LUDs than MODIS phenology product, possibly suggesting an overestimated greening potential of China's terrestrial ecosystems. Our study suggests that preseason temperature plays a previously neglected role in modeling spring LUD and instead of using VIs or temperature alone, a combination of temperature and VIs can improve the prediction of spring phenology. |
| WOS关键词 | LAND-SURFACE PHENOLOGY ; GREEN-UP DATE ; SPRING PHENOLOGY ; CLIMATE-CHANGE ; NORTH-AMERICA ; VEGETATION PHENOLOGY ; PRIMARY PRODUCTIVITY ; COVER CHANGE ; TEMPERATE ; SATELLITE |
| 资助项目 | national Key R&D program of China[2018YFA0606101] ; National Natural Science Foundation of China[41871255] ; Key Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000461535600007 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48627]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.South Dakota State Univ, Geospatial Sci Ctr Excellence, Dept Geog, 1021 Medary Ave,Wecota Hall 506B, Brookings, SD 57007 USA |
| 推荐引用方式 GB/T 7714 | Wang, Jian,Wu, Chaoyang,Wang, Xiaoyue,et al. A new algorithm for the estimation of leaf unfolding date using MODIS data over China's terrestrial ecosystems[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2019,149:77-90. |
| APA | Wang, Jian,Wu, Chaoyang,Wang, Xiaoyue,&Zhang, Xiaoyang.(2019).A new algorithm for the estimation of leaf unfolding date using MODIS data over China's terrestrial ecosystems.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,149,77-90. |
| MLA | Wang, Jian,et al."A new algorithm for the estimation of leaf unfolding date using MODIS data over China's terrestrial ecosystems".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 149(2019):77-90. |
入库方式： OAI收割
来源：地理科学与资源研究所
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