1. 农业部农业遥感机理与定量遥感重点实验室，北京农业信息技术研究中心，北京 100097
2. University of New South Wales, Sydney NSW 2052, Australia
3. 南京农业大学国家信息农业工程技术中心，江苏 南京 210095
4. 山东科技大学测绘科学与工程学院，山东 青岛 266590
5. 国家农业信息化工程技术研究中心，北京 100097
Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV
LIU Yang1, 4, 5, ZHANG Han2, FENG Hai-kuan1, 3, 5*, SUN Qian1, 5, HUANG Jue4, WANG Jiao-jiao1, 5, YANG Gui-jun1, 5
1. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. University of New South Wales, Sydney NSW 2052, Australia
3. National Information Agriculture Engineering Technology Center of Nanjing Agricultural University, Nanjing 210095, China
4. College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
5. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Abstract：Accurate monitoring of above-ground biomass (AGB) is an important part of farm production management, so rapid and accurate estimation of AGB is important for the development of precision agriculture. Traditionally, AGB has been obtained using destructive sampling methods, which makes large-area, long-term measurements difficult. However, with the advancement of science and technology, UAV hyperspectral remote sensing has become the most effective technical means to estimate AGB of large crops because of its advantages of high mobility, high spectral resolution and map integration. In this study, the canopy hyperspectral images and actual AGB data of potato tuber formation, tuber growth and starch accumulation stages were obtained by carrying imaging spectrometer sensors on the UAV platform and drying and weighing method, respectively. Correlation analysis method (CAM), random frog method (RFM) and Gaussian process regression bands analysis tool (GPR-BAT) were used to screen canopy original spectra (COS) and first-order derivative spectra (FDS) for sensitive wavelengths, respectively, combined with partial least squares regression (PLSR) and Gaussian process regression (GPR) techniques to establish AGB estimation models for each fertility period and the estimation effects of different models were compared. The results showed that (1) the effect of combining the two regression techniques based on the characteristic wavelengths screened by the same method for COS and FDS to estimate AGB all changed from good to bad from the tuber formation stage to the starch accumulation stage. (2) Based on the characteristic wavelengths screened by the three methods of FDS respectively, the models constructed by homogeneous regression techniques are more effective than those based on COS accordingly. (3) The number of characteristic wavelengths screened based on COS and FDS using CAM, RFM and GPR-BAT methods were 28, 12, 6 and 12, 23, 10 at the tuber formation stage, 32, 8, 2 and 18, 28, 4 at the tuber growth stage, and 30, 15, 3 and 21, 33, 5 at the starch accumulation stage, respectively. (4) The effect of sensitive wavelengths for AGB estimation based on COS and FDS screened by three methods at each reproductive stage were GPR-BAT, RFM and CAM in descending order. (5) The models based on sensitive wavelengths screened by FDS through the GPR-BAT method at each fertility stage combined with PLSR were more accurate and stable with R2 of 0. 67, 0. 73 and 0. 65, NRMSE of 16.63%, 15.84% and 20.81%, respectively. This study shows that AGB can be accurately estimated using UAV hyperspectral imaging technology, which provides scientific guidance and reference for achieving dynamic monitoring of potato crop growth.
Key words：Potato; UAV; Imaging hyperspectral; Random frog; Gaussian process regression; Above-ground biomass
刘 杨，张 涵，冯海宽，孙 乾，黄 珏，王娇娇，杨贵军. 无人机成像高光谱的马铃薯地上生物量估算[J]. 光谱学与光谱分析, 2021, 41(09): 2657-2664.
LIU Yang, ZHANG Han, FENG Hai-kuan, SUN Qian, HUANG Jue, WANG Jiao-jiao, YANG Gui-jun. Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2657-2664.
[1] LIU Yang, FENG Hai-kuan, HUANG Jue, et al（刘 杨，冯海宽，黄 珏，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2020, 36(23): 181.
[2] LIU Yang, FENG Hai-kuan, HUANG Jue, et al（刘 杨，冯海宽，黄 珏，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2021, 52(2): 188.
[3] Yue J B, Feng H K, Yang G J, et al. Remote Sensing, 2018, 10(2): 66.
[4] Tao H L, Feng H K, Xu L J, et al. Sensors, 2020, 20(5): 1296.
[5] Kanke Y, Tubana B, Dalen M, et al. Precision Agriculture, 2016, 17(5): 507.
[6] LIU Bin, REN Jian-qiang, CHEN Zhong-xin, et al（刘 斌，任建强，陈仲新，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2016, 32(16): 125.
[7] TAO Hui-lin, FENG Hai-kuan, XU Liang-ji, et al（陶惠林，冯海宽，徐良骥，等）. Jiangsu Journal of Agricultural Sciences（江苏农业学报）, 2020, 36(5): 1154.
[8] SHI Ya-jiao, CHEN Peng-fei（石雅娇，陈鹏飞）. Chinese Agricultural Science Bulletin（中国农学通报）, 2019, 35(17): 117.
[9] Liu N F, Townsend P A, Naber M R, et al. Remote Sensing of Environment, 2021, 255(3): 112303.
[10] Luo S J, He Y B, Li Q, et al. Journal of Applied Remote Sensing, 2020, 14(1): 1.
[11] SUN Hong, ZHENG Tao, LIU Ning, et al（孙 红，郑 涛，刘 宁，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2018, 34(1): 149.
[12] SUN Hong, LIU Ning, WU Li, et al（孙 红，刘 宁，吴 莉，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(3): 910.
[13] Fu Y Y, Yang G J, Li Z H, et al. Remote Sensing, 2020, 12(22): 3778.
