Scientia Agricultura Sinica ›› 2014, Vol. 47 ›› Issue (19): 3780-3790.doi: 10.3864/j.issn.0578-1752.2014.19.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & ECOLOGY • Previous Articles     Next Articles

Remote Sensing Prediction of Winter Wheat Protein Content Based on Nitrogen Translocation and GRA-PLS Method

LI Zhen-hai1,2,3, XU Xin-gang1,2, JIN Xiu-liang1,2,4, ZHANG Jing-cheng1,2, SONG Xiao-yu1,2, SONG Sen-nan1,2,4, YANG Gui-jun1,2, WANG Ji-hua1,2   

  1. 1 Beijing Research Center for Information Technology in Agriculture, Beijing 100097
    2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    3 Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310029
    4 Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, Jiangs
  • Received:2013-11-20 Revised:2014-05-12 Online:2014-10-01 Published:2014-10-01

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Abstract: 【Objective】Prediction of grain protein content (GPC) can provide effective decision-making supporting information for acquisition and processing of high quality wheat. The objective of the study is to demonstrate the feasibility of remote sensing monitoring of wheat grain protein content based on nitrogen translocation theory, and its expansibility between regional and annual level. 【Method】Field experiments of four winter wheat cultivars by four nitrogen applications in Beijing during 2012-2013 growing seasons were carried out for model building. Firstly, the two main sources of grain nitrogen accumulation and their relationships were analyzed based on nitrogen translocation theory and agronomy parameters modeling. The nitrogen remobilization from vegetative organs to grain was considered as the key point, while the nitrogen uptake from the root absorption during grain filling stage was simply calculated as the nitrogen remobilization from vegetative organs to grain multiplied by a factor. Mechanism of predicting GPC with leaf nitrogen content (LNC) at the flowering stage was clarified through integrating agronomy parameters modeling. Meanwhile, the temperature factor was considered. Secondly, twenty-four vegetative indices were selected according to the good relationship between vegetative indices and leaf nitrogen content, and remote sensing estimating of LNC was established by using grey relational method and partial least squares method (GRA-PLS). Therefore, a prediction model of GPC with remote sensing was established. 【Result】The results showed that the selected five vegetative indices according to grey relational grade were mND705, NDVIcanste, Readone, DCNI and NDCI. For the LNC estimating, the determination coefficient (R2) and corresponding to root mean square error (RMSE) of modeling and validation results were 0.859, 0.257% and 0.726, 0.063%, respectively. Estimation of LNC has good robustness by using GRA-PLS method. The R2 and RMSE of predicted and measured GPC of modeling and validation results were 0.726, 1.30% and 0.609, 1.19%, respectively. The results indicated that it was available to estimate GPC by integration model of nitrogen translocation theory and GRA-PLS method. 【Conclusion】The integration model with explanatory and expansibility could explain the theory of “why the LNC is used to predict GPC”, achieved prediction of grain protein content between regional and annual levels, and had a wide range of potential applications.

Key words: grain protein content, nitrogen translocation, grey relational method; partial least squares method, vegetation index

[1] 李少昆, 谭海珍, 王克如, 肖春华, 谢瑞芝, 高世菊. 小麦籽粒蛋白质含量遥感监测研究进展. 农业工程学报, 2009, 25(2): 302-307.
Li S K, Tan H Z, Wang K R, Xiao C H, Xie R Z, Gao S J. Research progress in wheat grain protein content monitoring using remote sensing. Transactions of the CSAE, 2009, 25(2): 302-307. (in Chinese)
[2] 王纪华, 李存军, 刘良云, 黄文江, 赵春江. 作物品质遥感监测预报研究进展. 中国农业科学, 2008, 41(9): 2633-2640.
Wang J H, Li C J, Liu L Y, Huang W J, Zhao C J. Progress of remote sensing monitoring and forecasting crop quality. Scientia Agricultura Sinica, 2008, 41(9): 2633-2640. (in Chinese)
[3] 黄文江, 王纪华, 刘良云, 赵春江, 宋晓宇, 马智宏. 冬小麦品质的影响因素及高光谱遥感监测方法. 遥感技术与应用, 2004, 19(3): 143-148.
Huang W J, Wang J H, Liu L Y, Zhao C J, Song X Y, Ma Z H. Study on grain quality effecting factors and monitoring methods by using hyperspectral data in winter wheat. Remote Sensing Technology and Application, 2004, 19(3): 143-148. (in Chinese)
[4] 王纪华, 黄文江, 赵春江, 杨敏华, 王之杰. 利用光谱反射率估算叶片生化组分和籽粒品质指标研究. 遥感学报, 2003, 7(4): 277-284.
Wang J H, Huang W J, Zhao C J, Yang M H, Wang Z J. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. Journal of Remote Sensing, 2003, 7(4): 277-284. (in Chinese)
[5] 曹广才, 王绍中. 小麦品质生态. 北京: 中国科学技术出版社, 1994.
Cao G C, Wang S Z. Wheat Ecology of Quality. Beijing: China Science and Technology Press, 1994. (in Chinese)
[6] 曹卫星, 姜东, 郭文善, 王龙俊. 小麦品质生理生态及调优技术. 北京: 中国农业出版社, 2005.
Cao W X, Jiang D, Guo W S, Wang L J. Wheat Quality Physiological Ecology and Tuning Techniques. Beijing: China Agriculture Press, 2005. (in Chinese)
[7] Matsunaka T, Watanabe Y, Miyawaki T, Lchikawa N. Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter. Soil Science and Plant Nutrition, 1997, 43(1): 127-134.
[8] Boegh E, Søgaard H, Broge N, Hasager C B, Jensen N O, Scheled K, Thomsen A. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 2002, 81(2): 179-193.
[9] Liu L Y, Wang J H, Bao Y S, Huang W J, Ma Z H, Zhao C J. Predicting winter wheat condition, grain yield and protein content using multi‐temporal EnviSat‐ASAR and Landsat TM satellite images. International Journal of Remote Sensing, 2006, 27(4): 737-753.
[10] Zhao C J, Liu L Y, Wang J H, Huang W J, Song X Y, Li C J. Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress. International Journal of Applied Earth Observation and Geoinformation, 2005, 7(1): 1-9.
[11] Wang Z J, Wang J H, Liu L Y, Huang W J, Zhao C J, Wang C Z. Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Research, 2004, 90(2): 311-321.
[12] 李映雪, 朱艳, 田永超, 尤小涛, 周冬琴, 曹卫星. 小麦冠层反射光谱与籽粒蛋白质含量及相关品质指标的定量关系. 中国农业科学, 2005, 38(7): 1332-1338.
Li Y X, Zhu Y, Tian Y C, You X T, Zhou D Q, Cao W X. Relationship of grain protein content and relevant quality traits to canopy reflectance spectra in wheat. Scientia Agricultra Sinica, 2005, 38(7): 1332-1338. (in Chinese)
[13] 田永超, 朱艳, 曹卫星, 范雪梅, 刘小军. 利用冠层反射光谱和叶片 SPAD 值预测小麦籽粒蛋白质和淀粉的积累. 中国农业科学, 2004, 37(6): 808-813.
Tian Y C, Zhu Y, Cao W X, Fan X M, Liu X J. Monitoring protein and starch accumulation in wheat grains with leaf SPAD and canopy spectral reflectance. Scientia Agricultra Sinica, 2004, 37(6): 808-813. (in Chinese)
[14] 李卫国, 王纪华, 赵春江, 刘良云, 宋晓宇, 童庆禧. 基于 NDVI 和氮素积累的冬小麦籽粒蛋白质含量预测模型. 遥感学报, 2008, 12(3): 506-514.
Li W G, Wang J H, Zhao C J, Liu L Y, Song X Y, Tong Q X. A model for predicting protein content in winter wheat grain based on Land-Sat TM image and nitrogen accumulation. Journal of Remote Sensing, 2008, 12(3): 506-514. (in Chinese)
[15] Reyniers M, Vrindts E, De Baerdemaeker J. Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat. European Journal of Agronomy, 2006, 24(2): 87-94.
[16] Pettersson C G, Eckersten H. Prediction of grain protein in spring malting barley grown in northern Europe. European Journal of Agronomy, 2007, 27(2): 205-214.
[17] 陈鹏飞, 王吉顺, 潘鹏, 徐于月, 姚凌. 基于氮素营养指数的冬小麦籽粒蛋白质含量遥感反演. 农业工程学报, 2011, 27(9): 75-80.
Chen P F, Wang J S, Pan P, Xu Y Y, Yao L. Remote detection of wheat grain protein content using nitrogen nutrition index. Transactions of the CSAE, 2011, 27(9): 75-80. (in Chinese)
[18] Li C J, Wang J H, Wang Q, Wang D C, Song X Y, Wang Y, Huang W  J. Estimating wheat grain protein content using multi-temporal remote sensing data based on partial least squares regression. Journal of Integrative Agriculture, 2012, 11(9): 1445-1452.
[19] 金秀良, 徐新刚, 李振海, 王纪华. 基于新型植被指数对冬小麦蛋白质含量的估算研究. 光谱学与光谱分析, 2013, 33(9): 2541-2545.
Jin X L, Xu X G, Li Z H, Wang J H. Estimation of winter wheat protein content based on new indexes and gray relational method. Spectroscopy and Spectral Analysis, 2013, 33(9): 2541-2545. (in Chinese)
[20] 于振文. 作物栽培学总论, 北京: 中国农业出版社, 2005.
Yu Z W. Overview of Crop Cultivation. Beijing: China Agriculture Press, 2005. (in Chinese)
[21] 曹卫星. 数字农作技术. 北京: 科学出版社, 2008.
Cao W X. Digital Farming Technology. Beijing: Science Press, 2008. (in Chinese)
[22] Pan J, Zhu Y, Jiang D, Dai T B, Li Y X, Cao W X. Modeling plant nitrogen uptake and grain nitrogen accumulation in wheat. Field Crops Research, 2006, 97(2): 322-336.
[23] Lemaire G, Jeuffroy M H, Gastal F. Diagnosis tool for plant and crop N status in vegetative stage: theory and practices for crop N management. European Journal of Agronomy, 2008, 28(4): 614-624.
[24] Matsunaka T, Watanabe Y, Miyawaki T, Ichikawa N. Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter. Soil Science and Plant Nutrition, 1997, 43(1): 127-134.
[25] Pearson R L, Miller D L. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proceedings of the English International Sysposium on Remote Sensing of Environment, 1972, 2: 1375-1381.
[26] Xue L H, Cao W X, Luo W H, Dai T B, Zhu Y. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal, 2004, 96(1): 135-142.
[27] Zhu Y, Yao X, Tian Y C, Liu X J, Cao W X. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(1): 1-10.
[28] Deering D W, Harlan J C. Monitoring the Vernal Advancement and Retrogradation (greenwave effect) of Natural Vegetation. Texas A & M University, Remote Sensing Center, 1974.
[29] Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 1991, 35(2): 161-173.
[30] Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 1995, 31(2): 221-230.
[31] Daughtry C S T, Walthall C L, Kim M S, De Colstoun E B, McMurtrey J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000, 74(2): 229-239.
[32] 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 Sensing of Environment, 2001, 76(2): 156-172.
[33] Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81(2): 416-426.
[34] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81(2): 337-354.
[35] Read J J, Tarpley L, McKinion J M, Reddy K R. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. Journal of Environmental Quality, 2002, 31(5): 1442-1452.
[36] Steddom K, Heidel G, Jones D, Rush C M. Remote detection of rhizomania in sugar beets. Phytopathology, 2003, 93(6): 720-726.
[37] Metternicht G. Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 2003, 24(14): 2855-2877.
[38] Dash J, Curran P J. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research, 2007, 39(1): 100-104.
[39] Gitelson A A, Vina A, Ciganda V, Rundquist D C, Arkebauer T J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 2005, 32, L080403. doi: 10.1029/2005GL022688
[40] Reyniers M, Walvoort D J J, De Baardemaaker J. A linear model to predict with a multi - spectral radiometer the amount of nitrogen in winter wheat. International Journal of Remote Sensing, 2006, 27(19): 4159-4179.
[41] Eitel J U H, Long D S, Gessler P E, Smith A M S. Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing, 2007, 28(18): 4183-4190.
[42] Chen P F, Haboudane D, Tremblay N, Wang J H, Vigneault P, Li B G. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 2010, 114(9): 1987-1997.
[43] Fitzgerald G, Rodriguez D, O’Leary G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index-the canopy chlorophyll content index (CCCI). Field Crops Research, 2010, 116(3): 318-324.
[44] Tian Y C, Yao X, Yang J, Cao W X, Hannaway D B, Zhu Y. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and space-based hyperspectral reflectance. Field Crops Research, 2011, 120(2): 299-310.
[45] Mishra S, Mishra D R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll - a concentration in turbid productive waters. Remote Sensing of Environment, 2012, 117: 394-406.
[46] Deng J L. Introduction to Grey Mathematics Resources Science. Wuhan: Huazhong University Press, 2010.
[47] 曹明霞. 灰色关联分析模型及其应用的研究[D]. 南京: 南京航空航天大学, 2007.
Cao M X. Research on grey incidence analysis model and its application [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007. (in Chinese)
[48] Geladi P, Kowalski B R. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 1986, 185: 1-17.
[49] 王纪华, 赵春江, 黄文江. 农业定量遥感基础与应用. 北京: 科学出版社, 2008.
Wang J H, Zhao C J, Huang W J. Quantitative Remote Sensing of Agriculture: Theory and Application. Beijing: Science Press, 2008. (in Chinese)
[50] 李卫国, 朱艳, 荆奇, 曹卫星. 水稻籽粒蛋白质积累的模拟模型研究. 中国农业科学, 2006, 39(3): 544-551.
Li W G, Zhu Y, Jing Q, Cao W X. Modeling protein accumulation in rice grain. Scientia Agricultura Sinica, 2006, 39(3): 544-551.
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