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Proceedings Paper

Winter wheat growth spatial variation monitoring through hyperspectral remote sensing image
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Paper Abstract

This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.

Paper Details

Date Published: 14 October 2015
PDF: 9 pages
Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96372A (14 October 2015); doi: 10.1117/12.2195276
Show Author Affiliations
Xiaoyu Song, Beijing Research Ctr. for Information Technology in Agriculture (China)
Ting Li, Hainan Normal Univ. (China)
Jihua Wang, Beijing Research Ctr. for Agri-food Testing and Farmland Monitoring (China)
Xiaohe Gu, Beijing Research Ctr. for Information Technology in Agriculture (China)
Xingang Xu, Beijing Research Ctr. for Information Technology in Agriculture (China)

Published in SPIE Proceedings Vol. 9637:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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