Welcome to Acta Agronomica Sinica,

Acta Agron Sin ›› 2017, Vol. 43 ›› Issue (04): 549-557.doi: 10.3724/SP.J.1006.2017.00549


Application of an Improved Method in Retrieving Leaf Area Index Combined Spectral Index with PLSR in Hyperspectral Data Generated by Unmanned Aerial Vehicle Snapshot Camera

GAO Lin1,2,YANG Gui-Jun1,*,LI Chang-Chun3,FENG Hai-Kuan1,XU Bo 1,WANG Lei1,3,DONG Jin-Hui1,3,FU Kui3   

  1. 1 Beijing Research Center for Information Technology in Agriculture / National Engineering Research Center for Information Technology in Agriculture / Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; 2 School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing in Jiangsu province 210023, China; 3 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo in Henan province 454000, China
  • Received:2016-05-15 Revised:2017-01-21 Online:2017-04-12 Published:2017-02-10
  • Contact: Yang Guijun, E-mail: yanggj@nercita.org.cn E-mail:gaol081115@126.com
  • Supported by:

    This study was supported by the National Key Research and Development Program of China (2016YFD0300602), the National Natural Science Foundation of China (61661136003, 41471285, 41271345), and the Innovation Capacity Building Project of Beijing Academy of Agriculture and Forestry Sciences (KJCX20170423).


The objective of this study was to demonstrate the value of an improved method of retrieved leaf area index (LAI) based on unmanned aerial vehicle (UAV) hyperspectral data combined spectral characteristics, as red edge parameters (REPs) and vegetation indices, with partial least squares regression (PLSR). We got UAV UHD185 hyperspectral images at booting, anthesis, and filling stages in winter wheat. And synchronously measured ASD hyperspectral data and winter wheat LAI. We compared UHD185 data with ASD data in terms of the correlation between reflectivity and vegetation indices to verify the UAV hyperspectral data accuracy. The band 3 to 96 (458-830 nm) of UHD185 hyperspectral data had better spectral quality and was suitable for detecting winter wheat LAI. We did correlation analysis between spectral characteristics, six kinds of vegetation indices and four kinds of red edge parameters, and LAI, and used two kinds of validation methods, independent validation and cross validation, to analyze the prediction accuracy of winter wheat LAI. Compared with traditional LAI fitting method, the improved LAI fitting method especially PLSR+REPs, greatly improved the prediction accuracy of winter wheat LAI. The above results confirmed that the improved LAI fitting method is able to better utilize UAV UHD185 hyperspectral data to predict LAI of winter wheat. Moreover, it is expected to provide a few new ideas for retrieving crop physical and chemical parameters based on UAV hyperspectral data.

Key words: Unmanned aerial vehicle (UAV), Hyperspectral remote sensing, Leaf area index (LAI), Partial least squares regression, Red edge parameters, Vegetation indices

[1] Baret F, Jacquemoud S, Guyot G, Leprieur C. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands. Remote Sens Environ, 1992, 41: 133–142 [2] Aparicio N, Villegas D, Araus J L, Casadesús J, Royo C. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci, 2002, 42: 1547–1555 [3] Coyne P I, Aiken R M, Maas S J, Lamm F R. Evaluating yield tracker forecasts for maize in western Kansas. Agron J, 2009, 101: 671–680 [4] De Souza N. Machines learn phenotypes. Nat Methods, 2013, 10: 38 [5] 王惠文, 吴载斌, 孟洁. 偏最小二乘回归的线性与非线性方法. 北京: 国防工业出版社, 2006 Wang H W, Wu Z B, Meng J. Partial least squares regression-linear and nonlinear methods. Beijing: National Defense Industry Press, 2006 (in Chinese) [6] Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ, 2003, 86: 542–553 [7] Nguyen H T, Lee B W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur J Agron, 2006, 24: 349–356 [8] Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogrammetry &Remote Sens, 2008, 63: 409–426 [9] 李小文, 高峰, 王锦地, 朱启疆. 遥感反演中参数的不确定性与敏感性矩阵. 遥感学报, 1997, 1(1): 5–14 Li X W, Gao F, Wang J D, Zhu Q J. Uncertainty and sensitivity matrix of parameters in inversion of physical BRDF model. J Remote Sens, 1997, 1(1): 5–14 (in Chinese with English abstract) [10] Li X C, Zhang Y J, Bao Y S, Luo J H, Jin X L, Xu X G, Song X Y, Yang G G. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sens, 2014, 6: 6221–6241 [11] 高林, 李长春, 王宝山, 杨贵军, 王磊, 付奎. 基于多源遥感数据的大豆叶面积指数估测精度对比. 应用生态学报, 2016, 27: 191–200 Gao L, Li C C, Wang B S, Yang G G, Wang L, Fu K. Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data. Chin J Appl Ecol, 2016, 27: 191–200 (in Chinese with English abstract) [12] Bareth G, Aasen H, Bendig J, Gnyp M L, Bolten A, Jung A, Michels R, Soukkam?ki J. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: spectral comparison with portable spectroradiometer measurements. Photogrammetrie-Fernerkundung-Geoinf, 2015, 2015: 69–79 [13] Aasen H, Burkart A, Bolten A, Bareth G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J Photogrammetry & Remote Sens, 2015, 108: 245–259 [14] Lucieer A, Malenovsky Z, Veness T, Wallace L. HyperUAS: imaging spectroscopy from a multirotor unmanned aircraft system. J Field Robot, 2014, 31: 571–590 [15] Turner D, Lucieer A, Wallace L. Direct georeferencing of ultrahigh-resolution UAV imagery. IEEE Trans Geosci & Remote Sens, 2014, 52: 2738–2745 [16] Pe?uelas J, Isla R, Filella I, Araus J L. Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Sci, 1997, 37: 198–202 [17] Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sens Environ, 1996, 55: 95–107 [18] Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ, 2001, 76: 156–172 [19] Qi J, Chehbouni A, Huete A R, Kerr Y H, Sorooshian S. A modified soil adjusted vegetation index. Remote Sens Environ, 1994, 48: 119–126 [20] Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Strachan I B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ, 2004, 90: 337–352 [21] Dawson T P, Curran P J. Technical note a new technique for interpolating the reflectance red edge position. Int J Remote Sens, 1998, 19: 2133–2139 [22] 冯伟, 朱艳, 姚霞, 田永超, 郭天财, 曹卫星. 利用红边特征参数监测小麦叶片氮素积累状况. 农业工程学报, 2009, 25(11): 194–201 Feng W, Zhu Y, Yao X, Tian Y C, Guo T C, Cao W X. Monitoring nitrogen accumulation in wheat leaf with red edge characteristics parameters. Trans CSAE, 2009, 25(11): 194–201 (in Chinese with English abstract) [23] Filella I, Penuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int J Remote Sens, 1994, 15: 1459–1470 [24] 田明璐, 班松涛, 常庆瑞, 由明明, 罗丹, 王力, 王烁. 基于低空无人机成像光谱仪影像估算棉花叶面积指数. 农业工程学报, 2016, 32(21): 102–108 Tian M L, Ban S T, Chang Q R, You M M, Luo D, Wang L, Wang S. Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index. Trans CSAE, 2016, 32(21): 102–108 [25] Stagakis S, Markos N, Sykioti O, Kyparissis A. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sens Environ, 2010, 114: 977–994 [26] Gonsamo A. Normalized sensitivity measures for leaf area index estimation using three-band spectral vegetation indices. Int J Remote Sens, 2011, 32: 2069–2080 [27] Lee K-S, Cohen W B, Kennedy R E, Maiersperger T K, Gower S T. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens Environ, 2004, 91: 508–520 [28] Yu K, Li F, Gnyp M L, Miao Y X, Bareth G, Chen X P. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J Photogrammetry & Remote Sens, 2013, 78: 102–115 [29] Herrmann I, Pimstein A, Karnieli A, Cohen Y, Alchanatis V, Bonfil D J. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sens Environ, 2011, 115: 2141–2151

[1] LI Jin-Min, CHEN Xiu-Qing, YANG Qi, SHI Liang-Sheng. Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data [J]. Acta Agronomica Sinica, 2021, 47(7): 1342-1350.
[2] WU Ya-Peng,HE Li,WANG Yang-Yang,LIU Bei-Cheng,WANG Yong-Hua,GUO Tian-Cai,FENG Wei. Dynamic model of vegetation indices for biomass and nitrogen accumulation in winter wheat [J]. Acta Agronomica Sinica, 2019, 45(8): 1238-1249.
[3] QI Bo,ZHANG Ning,ZHAO Tuan-Jie,XING Guang-Nan,ZHAO Jing-Ming*,GAI Jun-Yi*. Prediction of Leaf Area Index Using Hyperspectral Remote Sensing in Breeding Programs of Soybean [J]. Acta Agron Sin, 2015, 41(07): 1073-1085.
[4] FENG Wei,WANG Xiao-Yu,SONG Xiao,HE Li,WANG Yong-Hua,GUO Tian-Cai. Estimation of Severity Level of Wheat Powdery Mildew Based on Canopy Spectral Reflectance [J]. Acta Agron Sin, 2013, 39(08): 1469-1477.
[5] WU Qiong,QI Bo,ZHAO Tuan-Jie,YAO Xin-Feng,ZHU Yan,GAI Jun-Yi. A Tentative Study on Utilization of Canopy Hyperspectral Reflectance to Esti-mate Canopy Growth and Seed Yield in Soybean [J]. Acta Agron Sin, 2013, 39(02): 309-318.
[6] BAI Jun-Hua;LI Shao-Kun;WANG Ke-Ru;SHUI Xue-Yan;CHEN Bing. Estimation Models of Cotton Aboveground Fresh Biomass Based on Field Hyperspectral Remote Sensing [J]. Acta Agron Sin, 2007, 33(02): 311-316.
Full text



No Suggested Reading articles found!