气候变化领域集成服务门户(CCPortal): Advances in hyperspectral remote sensing of vegetation traits and functions

CCPortal

DOI	10.1016/j.rse.2020.112121
	Advances in hyperspectral remote sensing of vegetation traits and functions
	Zhang Y.; Migliavacca M.; Penuelas J.; Ju W.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	The functions and traits of plants are key to understanding and predicting the adaptation of ecosystems to environmental changes. Remote sensing has been used to monitor the status of vegetation across multiple spatial and temporal scales. The remote sensing of vegetation is now undergoing a paradigm shift from monitoring structural parameters to monitoring functional traits. In particular, recent advances in hyperspectral techniques of remote sensing provide an opportunity to map vegetation traits and functions over a range of scales. In this editorial, we first present the background of the recent advances in the remote sensing of vegetation traits and functions and solar-induced fluorescence (SIF) of chlorophyll. We then summarize eight of the papers in this special issue that focus on new remote-sensing techniques and algorithms developed for retrieving plant functional traits, such as pigment and nitrogen contents and functional parameters. These contributions cover two major scientific themes: (1) estimating and monitoring plant traits and functions and (2) interpreting and understanding remotely sensed SIF signals. The research in this special issue will improve the development of the satellite remote sensing of plant traits and functions, allowing for improved estimation of vegetation processes such as photosynthesis and its associated water and carbon cycles. © 2020
英文关键词	Hyperspectral; Remote sensing; Satellite; Solar-induced fluorescence (SIF) of chlorophyll; Vegetation traits and functions
语种	英语
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/179089
作者单位	International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China; Jiangsu Provincial Key Laboratory of Geographic Information Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, China; Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Jena, D-07745, Germany; CSIC, Global ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia 08193, Spain; CREAF, Cerdanyola, Catalonia 08193, Spain
推荐引用方式 GB/T 7714	Zhang Y.,Migliavacca M.,Penuelas J.,et al. Advances in hyperspectral remote sensing of vegetation traits and functions[J],2021,252.
APA	Zhang Y.,Migliavacca M.,Penuelas J.,&Ju W..(2021).Advances in hyperspectral remote sensing of vegetation traits and functions.Remote Sensing of Environment,252.
MLA	Zhang Y.,et al."Advances in hyperspectral remote sensing of vegetation traits and functions".Remote Sensing of Environment 252(2021).