|
|
|
|
|
|
Hyperspectral Estimation on Growth Status of Winter Wheat by Using the Multivariate Statistical Analysis |
WANG Chao, WANG Jian-ming, FENG Mei-chen, XIAO Lu-jie, SUN Hui, XIE Yong-kai, YANG Wu-de* |
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China |
|
|
Abstract Accurate and non-destructive estimation on the growth status of winter wheat is of significance. The consecutive two-years experiments of nitrogen application in 2011—2012 and 2012—2013 were performed to obtain the canopy spectra and the six growth status indicators of winter wheat (Leaf area index, LAI; Above ground dry biomass, AGDB; Above ground fresh biomass, AGFB; Plant water content, PWC; Chlorophyll density, CH.D; Accumulated nitrogen content, ANC). The principle component analysis (PCA) was implemented to construct the comprehensive growth indicator (CGI), which could potentially represent the growth status of winter wheat. Furthermore, the method of partial least square (PLSR) was applied on constructing the hyperspectral prediction models of all growth indicators and validating the accuracy of CGI. The results showed that the constructed CGI significantly correlated with all the growth status indicators of winter wheat, excepting for the PWC. It indicated that the CGI could represent most of the information for the six indicators and the CGI also could be used to stand for the growth status of winter wheat. Moreover, the model performance of CGI and other six indicators were further compared, and it showed that the PLSR model of CGI performed best than other six indicators with R2=0.802, RMSE=1.268, RPD=2.015. The CGI model was validated and proved to be more accurate and robust (R2=0.672, RMSE=1.732 and RPD=1.489). The study showed that the CGI constructed with the PCA method could represent the growth status of winter wheat and the CGI model based on the PLSR method could be used to estimate the growth status of winter wheat. It also indicated that the multivariate statistical analysis had great potential to be applied in the field of crops by using the hyperspectral technology.
|
Received: 2017-08-22
Accepted: 2018-01-10
|
|
Corresponding Authors:
YANG Wu-de
E-mail: sxauywd@126.com
|
|
[1] HE Yong, PENG Ji-yu, LIU Fei, et al(何 勇, 彭继宇, 刘 飞, 等). Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2015, 31(3): 174.
[2] Zhao G M, Miao Y X, Wang, H Y. et al. Field Crops Research, 2013, 154: 23.
[3] Hilker T, Lepine L, Coops N C. Agricultural and Forest Meteorology, 2012, 153: 124.
[4] Bajgain R, Kawasaki Y, Akamatsu Y, et al. Field Crops Research, 2015, 180: 221.
[5] Tian J, Philpot W D. Remote Sensing of Environment, 2015, 169: 280.
[6] Ali A M, Darvishzadeh R, Skidmore A K. Agricultural and Forest Meteorology, 2017, 236: 162.
[7] WANG Hong-bo, ZHAO Zi-qi, LIN Yi, et al(王宏博, 赵梓淇, 林 毅, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(5): 1489.
[8] Wang C, Feng M C, Yang W D, et al. PLoS One, 2017, 12(1): e0167679.
[9] Wang Y, Wang D J, Shi P H, et al. Plant Method, 2014, 10(36): 1.
[10] Yang X M, Xie H T, Drury C F, et al. European Journal of Soil Science, 2012, 63: 177.
[11] Zou X B, Zhao J W, Malcolm J W P, et al. Analytica Chimica Acta, 2010, 667: 14.
[12] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications. Beijing: Chemical Industry Press (北京: 化学工业出版社), 2011. 56.
[13] Nawar S, Buddenbaum H, Hill J,et al. Soil Tillage Research, 2016, 155: 510.
[14] Serbin S P, Dillaway D N, Kruger E L, et al. Journal of Experimental Botany, 2012, 63: 489.
[15] Chang C W, Larid D A. Soil Science, 2002, 167(2): 110.
[16] YE Qin, JIANG Xue-qin, LI Xi-can, et al(叶 勤, 姜雪芹, 李西灿, 等. ). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(3): 164. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[3] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[5] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[8] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[9] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[10] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[11] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[12] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[13] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[14] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[15] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
|
|
|
|