Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion
文献类型: 外文期刊
第一作者: Yang, Guijun
作者: Yang, Guijun;Zhao, Chunjiang;Sun, Chenhong;Yang, Guijun;Zhao, Chunjiang;Feng, Haikuan;Li, Zhenhai;Li, Heli;Pu, Ruiliang
作者机构:
关键词: leaf reflectance;chlorophyll;leaf nitrogen density;N-PROSPECT;partial least squares regression
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: 2015 年 9 卷
页码:
收录情况: SCI
摘要: Through its association with proteins and plant pigments, leaf nitrogen (N) plays an important regulatory role in photosynthesis, leaf respiration, and net primary production. However, the traditional methods of measurement leaf N are rooted in sample-based spectroscopy in laboratory. There is a big challenge of deriving leaf N from the nondestructive field measured leaf spectra. In this study, the original PROSPECT model was extended by replacing the absorption coefficient of chlorophyll in the original PROSPECT model with an equivalent N absorption coefficient to develop a nitrogen-based PROSPECT model (N-PROSPECT). N PROSPECT was evaluated by comparing the model-simulated reflectance values with the measured leaf reflectance values. The validated results show that the correlation coefficient (R) was 0.98 for the wavelengths of 400 to 2500 nm. Finally, N-PROSPECT was used to simulate leaf reflectance using different combinations of input parameters, and partial least squares regression (PLSR) was used to establish the relationship between the N-PROSPECT simulated reflectance and the corresponding leaf nitrogen density (LND). The inverse of the PLSR-based N-PROSPECT model was used to retrieve LND from the measured reflectance with a relatively high accuracy (R-2 = 0.77, RMSE = 22.15 mu g cm(-2)). This result demonstrates that the N-PROSPECT model established in this study can accurately simulate nitrogen spectral contributions and retrieve LND. The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
分类号:
- 相关文献
作者其他论文 更多>>
-
Recognition of wheat rusts in a field environment based on improved DenseNet
作者:Chang, Shenglong;Cheng, Jinpeng;Fan, Zehua;Ma, Xinming;Li, Yong;Zhao, Chunjiang;Chang, Shenglong;Yang, Guijun;Cheng, Jinpeng;Fan, Zehua;Yang, Xiaodong;Zhao, Chunjiang
关键词:Plant disease; Wheat rust; Image processing; Deep learning; Computer vision (CV); DenseNet
-
Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images
作者:Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Wang, Gengze;Chen, Riqiang;Yang, Guijun;Feng, Haikuan;Meng, Di;Jin, Hailiang;Ge, Xiaosan;Wang, Laigang;Feng, Haikuan
关键词:early-season rice mapping; spectral index (SI); synthetic aperture radar (SAR); Simple Non-Iterative Clustering (SNIC); time series filtering; K-Means; Jeffries-Matusita (JM) distance
-
GCVC: Graph Convolution Vector Distribution Calibration for Fish Group Activity Recognition
作者:Zhao, Zhenxi;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Zhao, Zhenxi;Yang, Xinting;Zhou, Chao;Zhao, Chunjiang;Liu, Jintao
关键词:Fish; Feature extraction; Activity recognition; Calibration; Adhesives; Training; Convolution; Graph convolution vector calibration; fish group activity; activity feature vector calibration; fish activity dataset
-
Adaptive precision cutting method for rootstock grafting of melons: modeling, analysis, and validation
作者:Chen, Shan;Zhao, Chunjiang;Chen, Shan;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang;Jiang, Kai;Zheng, Wengang;Jia, Dongdong;Zhao, Chunjiang
关键词:Melon; Grafting robot; Adaptive cutting; Rootstock pith cavity; Machine vision
-
Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation
作者:Yang, Chongshan;Li, Guanglin;Zhao, Chunjiang;Fu, Xinglan;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Zhao, Chunjiang;Dong, Daming;Yang, Chongshan;Jiao, Leizi;Wen, Xuelin;Lin, Peng;Duan, Dandan;Dong, Daming;Dong, Chunwang
关键词:Black tea fermentation; Volatile organic compounds; Proton transfer reaction mass spectrometry; Fourier transform infrared spectroscopy; Principal component analysis; Extreme learning machine
-
Comparison of three models for winter wheat yield prediction based on UAV hyperspectral images
作者:Xu, Xiaobin;Teng, Cong;Zhu, Hongchun;Li, Zhenhai;Teng, Cong;Feng, Haikuan;Zhao, Yu
关键词:hyperspectral imagery; unmanned aerial vehicle; winter wheat; yield prediction model; remote sensing
-
Navigation line extraction algorithm for corn spraying robot based on YOLOv8s-CornNet
作者:Guo, Peiliang;Diao, Zhihua;Ma, Shushuai;He, Zhendong;Zhao, Suna;Zhao, Chunjiang;Li, Jiangbo;Zhang, Ruirui;Yang, Ranbing;Zhang, Baohua
关键词:agricultural robotics; computer vision; deep learning; navigation line extraction; network lightweight