气候变化领域集成服务门户(CCPortal): Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?

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DOI	10.1016/j.rse.2021.112767
	Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?
	Chen, Litong; Zhang, Yi; Nunes, Matheus Henrique; Stoddart, Jaz; Khoury, Sacha; Chan, Aland H. Y.; Coomes, David A.
通讯作者	Coomes, DA (通讯作者)，Univ Cambridge, Conservat Res Inst, Cambridge CB2 3EA, England. ; Coomes, DA (通讯作者)，Univ Cambridge, Dept Plant Sci, Cambridge CB2 3EA, England.
发表日期	2022
ISSN	0034-4257
EISSN	1879-0704
卷号	269
英文摘要	Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains unclear whether universal statistical models can be developed to predict traits from spectral information, or whether recalibration is necessary as conditions vary. In particular, multiple leaf traits vary simultaneously across growing seasons, and it is an open question whether these temporal changes can be predicted successfully from hyperspectral data. To explore this question, monthly changes in 21 physiochemical leaf traits and plant spectra were measured for eight deciduous tree species from the UK. Partial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed. Physiochemical traits and spectra varied greatly over the growing season, although there was less variation among mature leaves harvested between June and September. Importantly, leaf spectroscopy was able to predict seasonal variations of most leaf traits accurately, with accuracies of prediction generally higher for mature leaves. However, for several traits, the PLSR estimation models varied among species, and a single PLSR model could not be used to make accurate species-level predictions. Our findings demonstrate that leaf spectra can successfully predict multiple functional foliar traits through the growing season, establishing one of the fundamentals for monitoring and mapping plant functional diversity in temperate forests from air- and spaceborne imaging spectroscopy.
关键词	NITROGEN USE EFFICIENCY IMAGING SPECTROSCOPY PHOTOSYNTHETIC CAPACITY GLOBAL PATTERNS QUERCUS-RUBRA FAGUS-CRENATA CANOPY AGE VARIABILITY ECOSYSTEM
英文关键词	Leaf spectra; Hyperspectral data; Leaf traits; Season; Partial least-squares regression (PLSR); Temperate trees
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000759670100001
来源期刊	REMOTE SENSING OF ENVIRONMENT
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254774
作者单位	[Chen, Litong; Zhang, Yi; Nunes, Matheus Henrique; Stoddart, Jaz; Khoury, Sacha; Chan, Aland H. Y.; Coomes, David A.] Univ Cambridge, Conservat Res Inst, Cambridge CB2 3EA, England; [Chen, Litong; Zhang, Yi; Nunes, Matheus Henrique; Stoddart, Jaz; Khoury, Sacha; Chan, Aland H. Y.; Coomes, David A.] Univ Cambridge, Dept Plant Sci, Cambridge CB2 3EA, England; [Chen, Litong] Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plant Biota, Xining 810008, Peoples R China; [Nunes, Matheus Henrique] Univ Helsinki, Dept Geosci & Geog, Helsinki 00014, Finland; [Stoddart, Jaz] Bangor Univ, Sch Nat Sci, Bangor LL57 2DG, Gwynedd, Wales
推荐引用方式 GB/T 7714	Chen, Litong,Zhang, Yi,Nunes, Matheus Henrique,et al. Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?[J]. 中国科学院西北生态环境资源研究院,2022,269.
APA	Chen, Litong.,Zhang, Yi.,Nunes, Matheus Henrique.,Stoddart, Jaz.,Khoury, Sacha.,...&Coomes, David A..(2022).Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?.REMOTE SENSING OF ENVIRONMENT,269.
MLA	Chen, Litong,et al."Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?".REMOTE SENSING OF ENVIRONMENT 269(2022).