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Proceedings Paper

Estimating growth height of winter wheat with remote sensing
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Paper Abstract

Height is one of important parameters for evaluating winter wheat growth. It can be not only used to indicate growth status of winter wheat, but also play a very important role in wheat growth environmental simulating models. Remote sensing images can reflect vegetation information and variation trend on different spatial scales, and using remote sensing has become a very important means of retrieving crop growth indices such as H(height), F(vegetation coverage fraction), LAI(leaf area index) and so on. In the paper, firstly LAI was estimated with a gradient-expansion algorithm by combining remote sensing images of Landsat5 TM with field data of winter wheat measured in Shunyi&Tongzhou District, Beijing in 2008, and then applied the dimidiate pixel model with NDVI (Normalized Difference Vegetation Index) from landsat5 TM to calculate F(vegetation coverage fraction), lastly taking the ratio of LAI and F as the factor built the model to estimate winter wheat growth height. The result displayed that the determinant coefficient R2 arrived at 0.48 between the field measured and the fit value by the wheat height estimating model, which showed it was feasible to apply the model with multispectral remote sensing images to estimate the wheat height.

Paper Details

Date Published: 22 October 2010
PDF: 6 pages
Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 782428 (22 October 2010); doi: 10.1117/12.864909
Show Author Affiliations
Xingang Xu, National Engineering Research Ctr. for Information Technology in Agriculture (China)
Jihua Wang, National Engineering Research Ctr. for Information Technology in Agriculture (China)
Cunjun Li, National Engineering Research Ctr. for Information Technology in Agriculture (China)
Xiaoyu Song, National Engineering Research Ctr. for Information Technology in Agriculture (China)
Wenjiang Huang, National Engineering Research Ctr. for Information Technology in Agriculture (China)


Published in SPIE Proceedings Vol. 7824:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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