|
|
|
|
|
|
| Estimation of Above-Ground Biomass of Potato Based on Wavelet Analysis |
| LIU Yang1, 2, 3, SUN Qian1, 3, FENG Hai-kuan1, 3*, YANG Fu-qin4 |
1. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4. College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China |
|
|
|
|
Abstract It is essential to estimate above-ground biomass (AGB) quickly and accurately, and AGB is an important indicator of crop growth evaluation and yield prediction. Due to the saturation of AGB in multiple growth periods estimated by traditional vegetation indexes (VIs). Therefore, the study attempts to use VIs combined high-frequency information extracted by image wavelet decomposition (IWD) based on discrete wavelet transform (DWT) technology and wavelet coefficients extracted by continuous wavelet transform (CWT) technology, explore the estimation capabilities of VIs, VIs+IWD and VIs+CWT for AGB. Firstly, the hyperspectral and digital images of the unmanned aerial vehicle (UAV) and measured AGB were acquired during the potato budding stage, tuber formation stage, tuber growth stage, and starch accumulation stage. Secondly, three high-frequency information were extractedby using digital images through IWD technology, wavelet coefficients were extracted by using hyperspectral reflectance through CWT technology and six hyperspectral vegetation indexes were constructed. Then, the correlation between vegetation index, high-frequency information and wavelet coefficients and AGB was analyzed, and the top 10 bands with high absolute values of correlation coefficients at different scales were selected. Finally, the partial least square regression (PLSR) was used to construct and compare AGB estimation models with VIs, VIs+IWD and VIs+CWT. The results showed that: (1) 6 vegetation indexes, 3 high-frequency information and 10 wavelet coefficients selected in each growth period were significantly correlated with AGB, and the correlation decreased after increased in the whole growth period, in which the wavelet coefficients was the highest, the nextwas high frequency information, and the vegetation index was the lowest. (2) The three estimation models of each growth period were compared and analyzed, the estimation effect of VIs+CWT was the best, and that of VIs was the worst, indicating that the model based on wavelet analysis has wide applicability and strong stability. (3) The AGB estimation models constructed by PLSR method with three variables in each growth period reached the highest accuracy in the tuber growth period (VIs: modeling R2=0.70, RMSE=98.88 kg·hm-2, NRMSE=11.63%; VIs+IWD: modeling R2=0.78, RMSE=86.45 kg·hm-2, NRMSE=10.17%; VIs+CWT: modeling R2=0.85, RMSE=74.25 kg·hm-2, NRMSE=9.27%). The PLSR method through VIs combined with IWD and CWT technology were used to improve the accuracy of AGB estimation, which provide a reliable reference for agricultural guidance and management.
|
|
Received: 2020-10-19
Accepted: 2021-01-30
|
|
|
|
Corresponding Authors:
FENG Hai-kuan
E-mail: fenghaikuan123@163.com
|
|
[1] TAO Hui-lin，XU Liang-ji，FENG Hai-kuan，et al（陶惠林，徐良骥，冯海宽，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2019，35（19）：107.
[2] ZHENG Yang，WU Bing-fang，ZHANG Miao（郑 阳，吴炳方，张 淼）. Journal of Remote Sensing（遥感学报），2017，21（2）：318.
[3] SHI Zhou，LIANG Zong-zheng，YANG Yuan-yuan（史 舟，梁宗正，杨媛媛，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2015，46（2）：247.
[4] Li W，Weiss M，Waldner F，et al. Remote Sensing，2015，7（10）：15494.
[5] TAO Hui-lin，FENG Hai-kuan，YANG Gui-jun，et al（陶惠林，冯海宽，杨贵军，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2020，51（1）：176.
[6] Zaman-allah M，Vergara O，Araus J L，et al. Plant Methods，2015，11（1）：1.
[7] Yue J B，Feng H K，Yang G J，et al. Remote Sensing，2018，10（1）：66.
[8] Tao H L，Feng H K，Xu L J，et al. Sensors，2020，20（5）：1296.
[9] Ma Y, Fang S, Peng Y, et al. Applied Sciences, 2019, 9（3）：545.
[10] Li B，Xu X M，Li Z，et al. ISPRS Journal of Photogrammetry and Remote Sensing，2020，162：161.
[11] Zheng H，Cheng T，Zhou M，et al. Precision Agriculture，2019，20（3）：611.
[12] LIU Chang，YANG Gui-jun，LI Zhen-hai，et al（刘 畅，杨贵军，李振海，等）. Scientia Agricultura Sinica（中国农业科学），2018，51（16）：3060. |
| [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] |
HOU Bing-ru1, LIU Peng-hui1, ZHANG Yang1, HU Yao-hua1, 2, 3*. Prediction of the Degree of Late Blight Disease Based on Optical Fiber Spectral Information of Potato Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1426-1432. |
| [3] |
JIANG Qing-hu1, LIU Feng1, YU Dong-yue2, 3, LUO Hui2, 3, LIANG Qiong3*, ZHANG Yan-jun3*. Rapid Measurement of the Pharmacological Active Constituents in Herba Epimedii Using Hyperspectral Analysis Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1445-1450. |
| [4] |
JI Tong1, 2, WANG Bo1, 2, YANG Jun-ying1, 2, LI Qiang1, 2, HE Guo-xing1, 2, PAN Dong-rong3, LIU Xiao-ni1, 2*. Spectral Characteristic Analysis and Spectral Identification of Desert Plants in Yanchi, Ningxia[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 678-685. |
| [5] |
FAN Nai-yun, LIU Gui-shan*, ZHANG Jing-jing, YUAN Rui-rui, SUN You-rui, LI Yue. Rapid Determination of TBARS Contents in Tan Mutton Using Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 713-718. |
| [6] |
LI Qing-bo1, WEI Yuan1, CUI Hou-xin2, FENG Hao2, LANG Jia-ye2. Quantitative Analysis of TOC in Water Quality Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 376-380. |
| [7] |
LU Qi-peng1, WANG Dong-min2*, SONG Yuan1*, DING Hai-quan3, GAO Hong-zhi3. Effect of Wavelength Drift on PLSR Calibration Model of Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 405-409. |
| [8] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 517-523. |
| [9] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
| [10] |
CHEN Yan-ling, CHENG Liang-lun*, WU Heng*, XU Li-min, HE Wei-jian, LI Feng. A Method of Terahertz Spectrum Material Identification Based on Wavelet Coefficient Graph[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3665-3670. |
| [11] |
DU Meng-meng1, Ali Roshanianfard2, LIU Ying-chao3. Inversion of Wheat Tiller Density Based on Visible-Band Images of Drone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3828-3836. |
| [12] |
JIANG Jing1, 2, ZHAO Zi-wei1, 2, CAI Chang1, 2, ZHANG Jin-song3, CHENG Zhi-qing1, 2*. Hyperspectral Estimation of Tea Leaves Water Content Under the Influence of Dust Retention[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3532-3537. |
| [13] |
ZHAO Yu-hui, LU Peng-cheng, LUO Yu-bo, SHAN Peng. NIR Calibration Transfer Method Based on Minimizing Mean Distribution Discrepancy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3051-3057. |
| [14] |
LIU Yang1, 4, 5, ZHANG Han2, FENG Hai-kuan1, 3, 5*, SUN Qian1, 5, HUANG Jue4, WANG Jiao-jiao1, 5, YANG Gui-jun1, 5. Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2657-2664. |
| [15] |
LI Zhi-yuan1,2, DENG Fan1*, HE Jun-liang2, WEI Wei1. Hyperspectral Estimation Model of Heavy Metal Arsenic in Soil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2872-2878. |
|
|
|
|
