气候变化领域集成服务门户(CCPortal): Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method

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DOI	10.1016/j.rse.2020.111933
	Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method
	Loozen Y.; Rebel K.T.; de Jong S.M.; Lu M.; Ollinger S.V.; Wassen M.J.; Karssenberg D.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy. © 2020 The Authors
英文关键词	Canopy nitrogen; Environmental predictors; Foliar nitrogen; ICP Forests; MERIS; MODIS; Plant traits; Random forests; Remote sensing; Vegetation indices
语种	英语
scopus关键词	Decision trees; Forecasting; Forestry; Nitrogen; Random forests; Remote sensing; Vegetation; Calibration data; Environmental variables; European forests; Photosynthetic enzymes; Remotely sensed data; Research objectives; Spatial patterns; Vegetation model; Mapping; accuracy assessment; calibration; canopy; carbon cycle; data assimilation; deciduous forest; environmental change; evergreen forest; forest ecosystem; mapping method; MERIS; MODIS; nitrogen; numerical method; remote sensing; spectral reflectance; vegetation index; Europe
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179224
作者单位	Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Earth Systems Research Center and Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH, United States
推荐引用方式 GB/T 7714	Loozen Y.,Rebel K.T.,de Jong S.M.,et al. Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method[J],2020,247.
APA	Loozen Y..,Rebel K.T..,de Jong S.M..,Lu M..,Ollinger S.V..,...&Karssenberg D..(2020).Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method.Remote Sensing of Environment,247.
MLA	Loozen Y.,et al."Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method".Remote Sensing of Environment 247(2020).