|
|
|
|
|
|
| Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices |
| LIU Shuang, YU Hai-ye, ZHANG Jun-he, ZHOU Hai-gen, KONG Li-juan, ZHANG Lei, DANG Jing-min, SUI Yuan-yuan* |
| School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China |
|
|
|
|
Abstract The accurate acquisition and prediction of chlorophyll content can provide a theoretical basis for precise management of crop planting. Optimal spectral index was used to establish the soybean chlorophyll content inversion model in this paper. The hyperspectral and chlorophyll content data of soybean flower bud differentiation were obtained. Firstly, seven typical spectral indices related to chlorophyll content were constructed, namely ratio index (RI), difference index (DI), normalized difference vegetation index (NDVI), modified simple ratio index (mSR), modified normalized difference index (mNDI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), respectively. First derivative (FD) processing was performed on the original hyper spectrum, and then the original and first derivative hyper spectrum are combined with all wavelengths in the full spectrum wavelength range to calculate 14 spectral indices. Then use the correlation matrix method to select the optimal spectral index. The correlation analysis was conducted between the spectral index calculated by all wavelength combinations and chlorophyll content. The maximum value of the correlation coefficient was taken as the index to extract the 14 optimal wavelength combinations, and the corresponding spectral index value was calculated as the optimal spectral index. Finally, the optimal spectral indices were divided into three groups as model input variables combined with the three methods of Partial least squares regression (PLS), Least squares support vector machine regression (LSSVM), and LASSO regression to model, then compare and analyze the results. The coefficients of determination R2c, R2p and the root mean square error RMSEC and RMSEP as model evaluation indicators, then soybean chlorophyll content inversion model with the highest accuracy, were finally selected. The results show that the 14 optimal spectral index wavelength combinations are RI (728, 727), DI (735, 732), NDVI (728, 727), mSR (728, 727), mNDI (728, 727), SAVI (728, 727), TVI (1 007, 708), FDRI (727, 708), FDDI (727, 788), FDNDVI (726, 705), FDmSR (726, 705), FDmNDI (726, 705), FDSAVI (727, 788) and FDTVI (760, 698), the maximum correlation coefficient with chlorophyll content are all greater than 0.8. The method to establish the optimal chlorophyll inversion model was the LSSVM modeling method combined with the first derivative spectral index (combination 2). The R2c=0.751 8, R2p=0.836 0, RMSEC=1.361 2, RMSEP=1.220 4, indicating that the model had high accuracy and could provide a reference for monitoring the growth status of soybean in a large area.
|
|
Received: 2020-07-24
Accepted: 2020-10-16
|
|
|
|
Corresponding Authors:
SUI Yuan-yuan
E-mail: suiyuan@jlu.edu.cn
|
|
[1] Lu X T，Lu S. International Journal of Remote Sensing，2015，36（5）：1447.
[2] Ramesh K，Sedigheh S，Nitya M，et al. PLOS ONE，2020，15(6)：e0233905.
[3] Tan K Z，Wang S W，Song Y Z，et al. Chemometrics and Intelligent Laboratory Systems，2018，172：68.
[4] Wang J J，Li Z K，Jin X L，et al. Computers and Electronics in Agriculture，2019，162：475.
[5] Cao Q，Miao Y X，Li F，et al. Precision Agriculture，2018，18(1)：2.
[6] Yang P Q，Christiaan V L，Campbell P K E，et al. Remote Sensing of Environment，2020，240: 111676.
[7] Xu X Q，Lu J S，Zhang N，et al. ISPRS Journal of Photogrammetry and Remote Sensing，2019，150：185.
[8] Román J R，Rodríguez-Caballero E，Rodríguez-Lozano B，et al. Remote Sensing，2019，11(11): 1.
[9] LIU Tan，XU Tong-yu，YU Feng-hua，et al（刘 潭，许童羽，于丰华，等）. Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2020，(5)：156.
[10] Alison D，Yu R，Chloe R. Annals of Forest Science，2020，77(2): 30.
[11] Bekele F，Korecha D，Negatu L. Journal of Agrometeorology，2017，19(2)：125.
[12] Sun H，Feng M C，Xiao L J，et al. PLOS ONE，2019，14(6)：e0216890.
[13] Lu J Z，Ehsani R，Shi Y Y. Scientific Reports，2018，8: 2793.
[14] NIJIATI Kamusi，SHI Qing-dong，WANG Jing-zhe，et al（尼加提·卡斯木，师庆东，王敬哲，等）. Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2017，33(22)：208. |
| [1] |
WANG Jin1, 2, CHEN Shu-tao1, 2*, DING Si-cheng1, 2, YAO Xue-wen1, 2, ZHANG Miao-miao1, 2, HU Zheng-hua2. Relationships Between the Leaf Respiration of Soybean and Vegetation
Indexes and Leaf Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1607-1613. |
| [2] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
| [3] |
YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min*. Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 866-872. |
| [4] |
JIANG Yan1, 2, MENG He1, ZHAO Yi-rong1, WANG Xian-xu1, WANG Sui1, XUE En-yu3, WANG Shao-dong1*. Rapid Analysis of Main Quality Parameters in Forage Soybean by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 919-923. |
| [5] |
TANG Yu-zhe, HONG Mei, HAO Jia-yong, WANG Xu, ZHANG He-jing, ZHANG Wei-jian, LI Fei*. Estimation of Chlorophyll Content in Maize Leaves Based on Optimized Area Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 924-932. |
| [6] |
HAN Yu1, SONG Shao-zhong2*, ZHANG Jia-huan3, TAN Yong1*, LIU Chun-yu1, ZHOU Yun-quan1, QU Guan-nan1, HAN Yan-li4, ZHANG Jing3, HU Yu3, MENG Wei-shi3, LIU Huan-jun5, ZHANG Yi-xiang1, LI Jia-yi1. Research on Soybean Bacterial Disease Markers Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 459-463. |
| [7] |
MAO Ya-chun1, WEN Jian1*, FU Yan-hua2, CAO Wang1, ZHAO Zhan-guo3, DING Rui-bo1. Quantitative Inversion Model Based on the Visible and Near-Infrared Spectrum for Skarn-Type Iron Ore[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 68-73. |
| [8] |
LI Li-jie1，2, YUE Yan-bin2, WANG Yan-cang3, ZHAO Ze-ying2, LI Rui-jun2, NIE Ke-yan2, YUAN Ling1*. The Quantitative Study on Chlorophyll Content of Hylocereus polyrhizus Based on Hyperspectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3538-3544. |
| [9] |
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076. |
| [10] |
LIU Tan1, 2, XU Tong-yu1, 2*, YU Feng-hua1, 2, YUAN Qing-yun1, 2, GUO Zhong-hui1, XU Bo1. Chlorophyll Content Estimation of Northeast Japonica Rice Based on Improved Feature Band Selection and Hybrid Integrated Modeling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2556-2564. |
| [11] |
GUI Jiang-sheng1, FEI Jing-yi1, FU Xia-ping2. Hyperspectral Imaging for Detection of Leguminivora Glycinivorella Based on 3D Few-Shot Meta-Learning Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2171-2174. |
| [12] |
Nigela Tuerxun1, Sulei Naibi2, GAO Jian3, SHEN Jiang-long1, ZHENG Jiang-hua1*, YU Dan-lin4. Chlorophyll Content Estimation of Jujube Leaves Based on GWLS-SVR Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1730-1736. |
| [13] |
XU Bo1, XU Tong-yu1, 2*, YU Feng-hua1, 2, ZHANG Guo-sheng1, FENG Shuai1, GUO Zhong-hui1, ZHOU Chang-xian1. Inversion Method for Cellulose Content of Rice Stem in Northeast Cold Region Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1775-1781. |
| [14] |
GUO Fei1, 2, XU Zhen3*, MA Hong-hong1, 2, LIU Xiu-jin1, 2, YANG Zheng1, 2, TANG Shi-qi1, 2. A Comparative Study of the Hyperspectral Inversion Models Based on the PCA for Retrieving the Cd Content in the Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1625-1630. |
| [15] |
CHANG Tao, XIE Xin, GUAN Mei, ZHANG Qiu-ping, ZHANG Zhen-qian*, GUAN Chun-yun. Analysis of the Relationship Between Hyperspectral Reflectance and Yield of Rape Under Different Micro Fertilizer Conditions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 552-557. |
|
|
|
|
