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| Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression |
| LI Cun-jun, WANG Ji-hua, WANG Qian, WANG Da-cheng, SONG Xiao-yu, WANG Yan, HUANG Wen-jiang |
1.Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China
2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China |
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摘要 Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of crossvalidation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
Abstract Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of crossvalidation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC (grain protein content) estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.
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Received: 11 August 2011
Accepted:
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| Fund: The project was supported by the National Natural Science Foundation of China (41171281, 40701120) and the Beijing Nova Program, China (2008B33). |
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Corresponding Authors:
LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn
E-mail: licj@nercita.org.cn
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| About author: LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn |
Cite this article:
LI Cun-jun, WANG Ji-hua, WANG Qian, WANG Da-cheng, SONG Xiao-yu, WANG Yan, HUANG Wen-jiang.
2012.
Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression. Journal of Integrative Agriculture, 12(9): 1445-1452.
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| [1]Badri B B, Apan A A, Kelly R M, Jensen T A, Strong W M, Butler D G. 2003. Relating satellite imagery with grain protein content. In: Proceedings of the Spatial Sciences Conference. Canberra, Australia. pp. 22-27. [2]Feilhauer H, Asner G P, Martin R E, Schmidtlein S. 2010. Brightness-normalized partial least squares regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 1947-1957. [3]Hansen P M, Jorgensen J R, Thomsen A. 2002. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. Journal of Agricultural Science, 139, 307-318. [4]Hansen P M, Schjoerring J K. 2003. Reflectance © 2012, CAAS. All rights reserved. Published by Elsevier Ltd. measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, 542-553. [5]Li L. 2008. Quantifying lunar soil composition with partial least squares modeling of reflectance. Advances in Space Research, 42, 267-274. [6]Li Y X, Zhu Y, Tian Y C, You X T, Zhou D Q, Cao W X. 2005. Relationship of grain protein content and relevant quality traits to canopy reflectance spectra in wheat. Scientia Agricultura Sinica, 38, 1332-1338. (in Chinese) [7]Liang S L. 2004. Quantitative Remote Sensing of Land Surfaces. Wiley and Sons, Hoboken, NJ, USA. Huang W J, Zhao C J, Wang J H, Wang J D, Ma Z H. 2004. Application of red edge variables to nutrition diagnosis and grain quality forecast of winter wheat. Transactions of the Chinese Society of Agricultural Engineering, 20, 1-6. (in Chinese) [8]Petterson C G, Eckersten H. 2007. Prediction of grain protein in spring malting barley growth in northern Europe. European Journal of Agronomy, 27, 205-214. [9]Reyniers M, Vrindts E, Baerdemaeker J D. 2006. Comparison of an aerial-based system and an on the ground continuous measuring device to predict yield of winter wheat. European Journal of Agronomy, 24, 87-94. [10]Song X Y, Wen J H, Wang J H, Liu L Y, Li C J. 2006. Preliminary application of ASTER images in winter wheat quality monitoring. Transactions of the Chinese Society of Agricultural Engineering, 9, 148-154. [11]Tian Y C. 2003. Monitoring water status in rice and growth parameters in rice and wheat with canopy spectral reflectance. Ph D thesis, Nanjing Agricultural University, China. (in Chinese) [12]Tobias R D. 1995. An introduction to partial least squares regression. In: Proceedings of the 20th Annual SAS Users Group International Conference, Cary, NC, USA. Wang J H, Huang W J, Zhao C J, Yang M H, Wang Z J. 2003. The inversion of leaf biochemical components and grain quality indicators of winter wheat with spectral reflectance. Journal of Remote Sensing, 7, 277-284. (in Chinese) [13]Wang J H, Zhao C J, Huang W J. 2008. Basis and Application of Agriculture Quantitative Remote Sensing. Science Press, Beijing, China. (in Chinese) Wang Z J, Wang J H, Liu L Y, Huang W J, Zhao C J, Wang C Z. 2004. Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crops Research, 90, 311-321. [14]Wei Y M. 2005. Corn Quality and Food Processing-Wheat Grain Quality and Food Processing. China Agricultural Science and Technology Press, Beijing, China. (in Chinese) Wolda S, Sjöströma M, Erikssonb L. 2001. PLS-regression: a basic tool of chemometric. Chemometrics and Intelligent Laboratory Systems, 58, 109-130. [15]Wright D L, Ritchie G, Rasmussen V P, Ramsey R D, Baker D. 2003. Managing protein in hard red spring wheat with remote sensing. In: The 6th Annual National Wheat Industry Research Forum, Hyatt Regency Albuquerque, New Mexico. [16]Xue L H, Zhu Y, Zhang X, Cao W X. 2004. Predicting wheat grain quality with canopy reflectance spectra. Acta Agronomica Sinica, 30, 1036-1041. (in Chinese) [17]Zhao C J, Liu L Y, Wang J H, Huang W J, Song X Y, Li C J. 2005. 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, 7, 1-9. |
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