DOI: 10.1016/j.jag.2016.12.005
Scopus记录号: 2-s2.0-85018864091
论文题名: Assessment of leaf carotenoids content with a new carotenoid index: Development and validation on experimental and model data
作者: Zhou X ; , Huang W ; , Kong W ; , Ye H ; , Dong Y ; , Casa R
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 57 起始页码: 24
结束页码: 35
语种: 英语
英文关键词: Hyperspectral
; Leaf carotenoids content
; Radiative transfer model
; Spectral index
Scopus关键词: canopy reflectance
; carotenoid
; data set
; experimental study
; field survey
; leaf area index
; model
; radiative transfer
; China
英文摘要: Leaf carotenoids content (LCar) is an important indicator of plant physiological status. Accurate estimation of LCar provides valuable insight into early detection of stress in vegetation. With spectroscopy techniques, a semi-empirical approach based on spectral indices was extensively used for carotenoids content estimation. However, established spectral indices for carotenoids that generally rely on limited measured data, might lack predictive accuracy for carotenoids estimation in various species and at different growth stages. In this study, we propose a new carotenoid index (CARI) for LCar assessment based on a large synthetic dataset simulated from the leaf radiative transfer model PROSPECT-5, and evaluate its capability with both simulated data from PROSPECT-5 and 4SAIL and extensive experimental datasets: the ANGERS dataset and experimental data acquired in field experiments in China in 2004. Results show that CARI was the index most linearly correlated with carotenoids content at the leaf level using a synthetic dataset (R2 = 0.943, RMSE = 1.196 μg/cm2), compared with published spectral indices. Cross-validation results with CARI using ANGERS data achieved quite an accurate estimation (R2 = 0.545, RMSE = 3.413 μg/cm2), though the RBRI performed as the best index (R2 = 0.727, RMSE = 2.640 μg/cm2). CARI also showed good accuracy (R2 = 0.639, RMSE = 1.520 μg/cm2) for LCar assessment with leaf level field survey data, though PRI performed better (R2 = 0.710, RMSE = 1.369 μg/cm2). Whereas RBRI, PRI and other assessed spectral indices showed a good performance for a given dataset, overall their estimation accuracy was not consistent across all datasets used in this study. Conversely CARI was more robust showing good results in all datasets. Further assessment of LCar with simulated and measured canopy reflectance data indicated that CARI might not be very sensitive to LCar changes at low leaf area index (LAI) value, and in these conditions soil moisture influenced the LCar retrieval accuracy. © 2016
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79931
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Department of Agricultural and Forestry scieNcEs (DAFNE), Universita’ della Tuscia, via San Camillo de Lellis, Viterbo, Italy
Recommended Citation:
Zhou X,, Huang W,, Kong W,et al. Assessment of leaf carotenoids content with a new carotenoid index: Development and validation on experimental and model data[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,57