Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data
文献类型: 外文期刊
作者: Kong, Weiping 1 ; Huang, Wenjiang 1 ; Zhou, Xianfeng 1 ; Song, Xiaoyu 3 ; Casa, Raffaele 4 ;
作者机构: 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China
2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, 11 Shuguang Hua Yuan Middle Rd, Beijing 100097, Peoples R China
4.Univ Tuscia, Dept Agr & Forestry Sci, Via San Camillo de Lellis, I-01100 Viterbo, Italy
关键词: carotenoid triangle ratio index;hyperspectral remote sensing;vegetation index;carotenoid content;chlorophyll content
期刊名称:JOURNAL OF APPLIED REMOTE SENSING ( 影响因子:1.53; 五年影响因子:1.565 )
ISSN: 1931-3195
年卷期: 2016 年 10 卷
页码:
收录情况: SCI
摘要: Precise estimation of carotenoids (Car) content in plants, from remotely sensed data, is challenging due to their small proportion in the overall total pigment content and to the overlapping of spectral absorption features with chlorophyll (Chl) in the blue region of the spectrum. The use of narrow band vegetation indices (VIs) obtained from hyperspectral data has been considered an effective way to estimate Car content. However, VIs have proved to lack sensitivity to low or high Car content in a number of studies. In this study, the carotenoid triangle ratio index (CTRI), derived from the existing modified triangular vegetation index and a single band reflectance at 531 nm, was proposed and employed to estimate Car canopy content. We tested the potential of three categories of hyperspectral indices earlier proposed for Car, Chl, Car/Chl ratio estimation, and the new CTRI index, for Car canopy content assessment in winter wheat and corn. Spectral reflectance representing plant canopies were simulated using the PROSPECT and SAIL radiative transfer model, with the aim of analyzing saturation effects of these indices, as well as Chl effects on the relationship between spectral indices and Car content. The result showed that the majority of the spectral indices tested, saturated with the increase of Car canopy content above 28 to 64 mu g/cm(2). Conversely, the CTRI index was more robust and was linearly and highly sensitive to Car content in winter wheat and corn datasets, with coefficients of determination of 0.92 and 0.75, respectively. The corresponding root mean square error of prediction were 6.01 and 9.70 mu g/cm(2), respectively. Furthermore, the CTRI index did not show a saturation effect and was not greatly influenced by changes of Chl values, outperforming all the other indices tested. Estimation of Car canopy content using the CTRI index provides an insight into diagnosing plant physiological status and environmental stress. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
- 相关文献
作者其他论文 更多>>
-
Remote sensing of quality traits in cereal and arable production systems: A review
作者:Li, Zhenhai;Fan, Chengzhi;Li, Zhenhai;Zhao, Yu;Song, Xiaoyu;Yang, Guijun;Jin, Xiuliang;Casa, Raffaele;Huang, Wenjiang;Blasch, Gerald;Taylor, James;Li, Zhenhong
关键词:Remote sensing; Quality traits; Grain protein; Cereal
-
Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation
作者:Sun, Heguang;Shu, Meiyan;Yue, Jibo;Guo, Wei;Sun, Heguang;Zhang, Jie;Feng, Ziheng;Feng, Haikuan;Song, Xiaoyu;Zhou, Lin
关键词:peanut southern blight; SMOTE; hyperspectral reflectance; machine learning; FOD
-
Rice yield and quality estimation coupling hierarchical linear model with remote sensing
作者:Xue, Hanyu;Xu, Xingang;Zhu, Qingzhen;Li, Yafeng;Xue, Hanyu;Xu, Xingang;Meng, Yang;Long, Huiling;Li, Heli;Song, Xiaoyu;Yang, Guijun;Yang, Min;Li, Yafeng;Jiang, Xiangtai
关键词:Hierarchical linear model; Rice yield and quality; Estimation model
-
Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data
作者:Feng, Ziheng;Ma, Xinming;Feng, Ziheng;Cheng, Zhida;Ren, Lipeng;Liu, Bowei;Zhang, Chengjian;Zhao, Dan;Sun, Heguang;Feng, Haikuan;Long, Huiling;Xu, Bo;Yang, Hao;Song, Xiaoyu;Yang, Guijun;Zhao, Chunjiang
关键词:UAV; Real-time; Composite index; Maize phenology; BBCH
-
Improved potato AGB estimates based on UAV RGB and hyperspectral images
作者:Liu, Yang;Feng, Haikuan;Fan, Yiguang;Chen, Riqiang;Bian, Mingbo;Ma, Yanpeng;Song, Xiaoyu;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang
关键词:UAV; VIs; High -frequency information; Wavelet coefficients; AGB
-
Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning
作者:Ma, Yuanyuan;Qiu, Chunxia;Ma, Yuanyuan;Zhang, Jie;Pan, Di;Zheng, Chunkai;Sun, Heguang;Feng, Haikuan;Song, Xiaoyu
关键词:leaf chlorophyll content; hyperspectral; hybrid methods; Gaussian process regression; active learning
-
Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements
作者:Liu, Yang;Feng, Haikuan;Fan, Yiguang;Bian, Mingbo;Ma, Yanpeng;Song, Xiaoyu;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang
关键词:Above-ground biomass; Vegetation indices; Textural features; Structural features; Gaussian process regression



