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Proceedings Paper

Discrimination of wheat and oat crops using field hyperspectral remote sensing
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Paper Abstract

In this study we attempt to identify the most suitable spectral bands to discriminate among wheat and oat crops using field hyperspectral remote sensing. Discrimination of these crops using ordinary aerial or multispectral satellite imagery can be challenging. Even though multispectral images could have a high spatial resolution, their few wide spectral bands hinder crop discrimination. Therefore, both high spatial resolution and spectral resolution are necessary to accurately discriminate between visually similar crops. One field each of oats and spring wheat, each at least 10 acres in size, was selected in southeastern Wisconsin. Biweekly spectral readings were taken using a spectroradiometer during the growing season from May to July. In each field, seven 10 m x 10 m quadrants were randomly placed and in each quadrants five points were selected from which 20 radiometric readings were taken. Radiometric measurements taken at each sampling point were averaged to derive a single reflectance curve per sampling date, covering the spectral range of 300 nm to 2,500 nm. Each spectral curve was divided into hyperspectral bands each 3 nm wide. The Mann-Whitney U-test was used to estimate how separable the two crops were. Results show that selected regions of the visible light and infrared radiation spectrum have the potential to discriminate between these crops. Crop discrimination is one of the first steps to support crop monitoring and agricultural surveys efforts.

Paper Details

Date Published: 28 April 2017
PDF: 6 pages
Proc. SPIE 10213, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017, 102130J (28 April 2017); doi: 10.1117/12.2266219
Show Author Affiliations
Allison Kaiser, Univ. of Wisconsin-Whitewater (United States)
Rocio Duchesne-Onoro, Univ. of Wisconsin-Whitewater (United States)


Published in SPIE Proceedings Vol. 10213:
Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2017
David P. Bannon, Editor(s)

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