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Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion
Sun, Jia1; Shi, Shuo1,2; Yang, Jian3; Chen, Biwu1; Gong, Wei1,2; Du, Lin3; Mao, Feiyue1,2,4; Song, Shalei4,5
2018-06-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
Volume212Pages:1-7
SubtypeArticle
AbstractChlorophyll (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.
KeywordHyperspectral Lidar Chlorophyll Content Prospect Model
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Technology
Funding OrganizationNational 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)
DOI10.1016/j.rse.2018.04.024
WOS KeywordOPTICAL-PROPERTIES MODEL ; REMOTE ESTIMATION ; NITROGEN ; REFLECTANCE ; CROP ; DIFFERENTIATION ; PERFORMANCE ; ALGORITHMS ; RETRIEVAL ; SPECTRA
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000435053200001
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.wipm.ac.cn/handle/112942/11977
Collection高技术创新与发展中心
Affiliation1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
2.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China
3.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China
4.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China
5.Chinese Acad Sci, Wuhan Inst Phys & Math, Wuhan 430071, Hubei, Peoples R China
Recommended Citation
GB/T 7714
Sun, Jia,Shi, Shuo,Yang, Jian,et al. Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion[J]. REMOTE SENSING OF ENVIRONMENT,2018,212:1-7.
APA Sun, Jia.,Shi, Shuo.,Yang, Jian.,Chen, Biwu.,Gong, Wei.,...&Song, Shalei.(2018).Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion.REMOTE SENSING OF ENVIRONMENT,212,1-7.
MLA Sun, Jia,et al."Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion".REMOTE SENSING OF ENVIRONMENT 212(2018):1-7.
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