|
|
|
|
|
|
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] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[2] |
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. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
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. |
[5] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[6] |
ZHENG Shu-yuan1, 2, HAI Yan1, 2, HE Meng-qi1, 2, WANG Jian-xiong1, 2. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517. |
[7] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[8] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[9] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[10] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
[11] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[12] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
[13] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[14] |
MA Bao-dong, YANG Xiang-ru, JIANG Zi-wei, CHE De-fu. Influence and Quantitative Analysis of Coal Dust Retention on Reflectance Spectra and Vegetation Index of Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1947-1952. |
[15] |
REN Hong-rui1, 2, ZHANG Yue-qi2, HE Qi-jin3, LI Rong-ping1, ZHOU Guang-sheng4, 5*. Extraction of Pddy Rice Planting Area Based on Multi-Temporal FY-3 MERSI Remote Sensing Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1606-1611. |
|
|
|
|