Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

doi:10.1016/S1671-2927(00)8676

Journal of Integrative Agriculture ›› 2012, Vol. 12 ›› Issue (9): 1445-1452.DOI: 10.1016/S1671-2927(00)8676

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

收稿日期:2011-08-11 出版日期:2012-09-01 发布日期:2012-09-09
通讯作者: LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn
作者简介:LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn
基金资助:
The project was supported by the National Natural Science Foundation of China (41171281, 40701120) and the Beijing Nova Program, China (2008B33).

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

Received:2011-08-11 Online:2012-09-01 Published:2012-09-09
Contact: LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn
About author:LI Cun-jun, Tel: +86-10-51503692, Fax: +86-10-51503750, E-mail: licj@nercita.org.cn
Supported by:
The project was supported by the National Natural Science Foundation of China (41171281, 40701120) and the Beijing Nova Program, China (2008B33).

摘要/Abstract

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.

关键词: grain protein content, agronomic parameters, multi-temporal, Landsat, partial least squares regression

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.

Key words: grain protein content, agronomic parameters, multi-temporal, Landsat, partial least squares regression

LI Cun-jun, WANG Ji-hua, WANG Qian, WANG Da-cheng, SONG Xiao-yu, WANG Yan, HUANG Wen-jiang. Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression[J]. Journal of Integrative Agriculture, 2012, 12(9): 1445-1452.

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Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

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