Paper
22 October 2010 Estimating growth height of winter wheat with remote sensing
Author Affiliations +
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xingang Xu, Jihua Wang, Cunjun Li, Xiaoyu Song, and Wenjiang Huang "Estimating growth height of winter wheat with remote sensing", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 782428 (22 October 2010); https://doi.org/10.1117/12.864909
Lens.org Logo
CITATIONS
Cited by 7 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Remote sensing

Vegetation

Earth observing sensors

Landsat

Data modeling

Multispectral imaging

Environmental sensing

Back to Top