Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion

doi:10.1016/j.rse.2018.04.024

CORC > 武汉物理与数学研究所 > 中国科学院武汉物理与数学研究所 > 高技术创新与发展中心

	Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion
	Gong, Wei 2,4; Sun, Jia 2; Shi, Shuo 2,4; Yang, Jian 3; Chen, Biwu 2; Song, Shalei 1,5; Mao, Feiyue 1,2,4; Du, Lin 3
刊名	REMOTE SENSING OF ENVIRONMENT
	2018-06-01
卷号	212 页码:1-7
关键词	Hyperspectral Lidar Chlorophyll Content Prospect Model
DOI	10.1016/j.rse.2018.04.024
文献子类	Article
英文摘要	Chlorophyll (Chl) is an important indicator of photosynthetic capacity and stress of vegetation. Remote sensing provides fast and nondestructive methods for estimating leaf Chl content based on its optical characteristics in visible and near-infrared spectrum. Multispectral lidar (MSL) systems have been developed to combine spectral and spatial detection abilities. Statistical relationships of plant biochemical constituents can be established through MSL measurements. However, empirical models cannot be readily extended to independent datasets. Simultaneously, the few spectral bands of MSL limit the use of a physical model. Hence, the development of hyperspectral lidar (HSL) systems offers a wider range of spectrum. This study investigated the possibility of adopting an HSL system with 32 channels covering 539-910 nm to estimate foliar Chl through a physical model. This study aimed to (1) Determine whether reflectance at the 32 channels is sufficient to retrieve Chl content through PROSPECT model inversion and (2) Considering the difference between passively and actively measured reflectance, investigate whether HSL measurements can be applied into PROSPECT model inversion for leaf biochemical constituents. Three kinds of datasets were used: a synthetic dataset simulated by running the PROSPECT model in forward mode, a public dataset ANGERS taking the channels of the HSL system, and an experimental dataset of paddy rice measured by the HSL system. Results showed HSL measurements can be directly used to retrieve leaf Chl content through PROSPECT-4 model inversion (R-2 = 0.55). These measurements also exhibit higher accuracy than that of support vector regression (threefold cross validation; 100 repetitions: median R-2 = 0.47). This validation work provides basis in the determination of vegetation physiological status directly from HSL measurements through model inversion with the PROSPECT model.
WOS关键词	OPTICAL-PROPERTIES MODEL ; REMOTE ESTIMATION ; NITROGEN ; REFLECTANCE ; CROP ; DIFFERENTIATION ; PERFORMANCE ; ALGORITHMS ; RETRIEVAL ; SPECTRA
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
WOS记录号	WOS:000435053200001
资助机构	National Natural Science Foundation of China(41601360) ; National Natural Science Foundation of China(41601360) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; China University of Geosciences (Wuhan)(CUG170661) ; China University of Geosciences (Wuhan)(CUG170661) ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University(17R05) ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University(17R05) ; National Natural Science Foundation of China(41601360) ; National Natural Science Foundation of China(41601360) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Wuhan Morning Light Plan of Youth Science and Technology(2017050304010308) ; Fundamental Research Funds for the Central Universities ; Fundamental Research Funds for the Central Universities ; China University of Geosciences (Wuhan)(CUG170661) ; China University of Geosciences (Wuhan)(CUG170661) ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University(17R05) ; Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University(17R05)
内容类型	期刊论文
源URL	[http://ir.wipm.ac.cn/handle/112942/11977]
专题	武汉物理与数学研究所_高技术创新与发展中心
作者单位	1.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China 2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China 3.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China 4.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China 5.Chinese Acad Sci, Wuhan Inst Phys & Math, Wuhan 430071, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Gong, Wei,Sun, Jia,Shi, Shuo,et al. Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion[J]. REMOTE SENSING OF ENVIRONMENT,2018,212:1-7.
APA	Gong, Wei.,Sun, Jia.,Shi, Shuo.,Yang, Jian.,Chen, Biwu.,...&Du, Lin.(2018).Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion.REMOTE SENSING OF ENVIRONMENT,212,1-7.
MLA	Gong, Wei,et al."Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion".REMOTE SENSING OF ENVIRONMENT 212(2018):1-7.