IGSNRR OpenIR
A new algorithm for the estimation of leaf unfolding date using MODIS data over China's terrestrial ecosystems
Wang, Jian1,2; Wu, Chaoyang2,3; Wang, Xiaoyue3; Zhang, Xiaoyang4
2019-03-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume149Pages:77-90
Corresponding AuthorWu, Chaoyang(wucy@igsnrr.ac.cn) ; Wang, Xiaoyue(wangxy@igsnrr.ac.cn)
AbstractUsing 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.
KeywordRemote sensing Spring phenology EVI2 Preseason temperature PFT
DOI10.1016/j.isprsjprs.2019.01.017
WOS KeywordLAND-SURFACE PHENOLOGY ; GREEN-UP DATE ; SPRING PHENOLOGY ; CLIMATE-CHANGE ; NORTH-AMERICA ; VEGETATION PHENOLOGY ; PRIMARY PRODUCTIVITY ; COVER CHANGE ; TEMPERATE ; SATELLITE
Indexed BySCI
Language英语
Funding ProjectNational Key R&D program of China[2018YFA0606101] ; National Natural Science Foundation of China[41871255] ; Key Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011]
Funding OrganizationNational Key R&D program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000461535600007
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48628
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chaoyang; Wang, Xiaoyue
Affiliation1.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.South Dakota State Univ, Geospatial Sci Ctr Excellence, Dept Geog, 1021 Medary Ave,Wecota Hall 506B, Brookings, SD 57007 USA
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Jian]'s Articles
[Wu, Chaoyang]'s Articles
[Wang, Xiaoyue]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Jian]'s Articles
[Wu, Chaoyang]'s Articles
[Wang, Xiaoyue]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Jian]'s Articles
[Wu, Chaoyang]'s Articles
[Wang, Xiaoyue]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.