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A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index
Yuan, Huanhuan1,2; Wu, Chaoyang1,2; Lu, Linlin3; Wang, Xiaoyue1,2
2018-10-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume144Pages:390-399
Corresponding AuthorWu, Chaoyang(wucy@igsnrr.ac.cn) ; Lu, Linlin(lull@radi.ac.cn)
AbstractAccurate estimation of vegetation phenology (the start/end of growing season, SOS/EOS) is important to understand the feedbacks of vegetation to meteorological circumstances. Because the evergreen forests have limited change in greenness, there are relatively less study to predict evergreen conifer forests phenology, especially for EOS in autumn. Using 11-year (2000-2010) records of MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), together with gross primary production (GPP) and temperature data at five evergreen conifer forests flux sites in Canada, we comprehensively evaluated the performances of several variables in modeling flux-derived EOS. Results showed that neither NDVI nor EVI can be used to predict EOS as they had no significant correlation with ground observations. In comparison, temperature had a better predictive strength for EOS, and R-2 between EOS and mean temperature (T-mean), the maximum temperature (T-max, daytime temperature) and the minimum temperature (T-min, nighttime temperature) were 0.45 (RMSE = 5.1 days), 0.32 (RMSE = 5.7 days) and 0.58 (RMSE = 4.6 days), respectively. These results suggest an unreported role of nighttime temperature in regulating EOS of evergreen forests, in comparison with previous study showing leaf-out in spring by daytime temperature. Furthermore, we demonstrated that it may be because nighttime temperature has a higher relationship with soil temperature (T-s) (R-2 = 0.67, p < 0.05). We then developed a new model combining T-min and EVI, which improved EOS modeling greatly both for these five flux sites and also for data collected at nine PhenoCam sites. Our results imply that the accuracy of current remote sensing VI estimated EOS should be used cautiously. In particular, we revealed the usefulness of nighttime temperature in modeling EOS of evergreen forests, which may be of potential importance for future ecosystem models.
KeywordPhenology Evergreen conifer forests T-min T-max NDVI/EVI
DOI10.1016/j.isprsjprs.2018.08.013
WOS KeywordDIGITAL REPEAT PHOTOGRAPHY ; CONTIGUOUS UNITED-STATES ; LAND-SURFACE PHENOLOGY ; SPRING PHENOLOGY ; INTERANNUAL VARIABILITY ; NORTHERN-HEMISPHERE ; AUTUMN PHENOLOGY ; ECOSYSTEM PRODUCTIVITY ; CARBON EXCHANGES ; CLIMATE-CHANGE
Indexed BySCI
Language英语
Funding ProjectNational Key R&D program of China[2018YFA0606101] ; Key Research Program of Frontier Sciences, CAS[QYZDB-SSW-DQC011] ; National Natural Science Foundation of China[41522109]
Funding OrganizationNational Key R&D program of China ; Key Research Program of Frontier Sciences, CAS ; National Natural Science Foundation of China
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000447109900028
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52694
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chaoyang; Lu, Linlin
Affiliation1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Digital Earth Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
Recommended Citation
GB/T 7714
Yuan, Huanhuan,Wu, Chaoyang,Lu, Linlin,et al. A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2018,144:390-399.
APA Yuan, Huanhuan,Wu, Chaoyang,Lu, Linlin,&Wang, Xiaoyue.(2018).A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,144,390-399.
MLA Yuan, Huanhuan,et al."A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 144(2018):390-399.
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