Mapping crop ground cover using airborne multispectral digital imagery Academic Article

abstract

• Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values. 2009 Springer Science+Business Media, LLC.

authors

• Rajan, Nithya

published proceedings

• PRECISION AGRICULTURE

author list (cited authors)

• Rajan, N., & Maas, S. J.

citation count

• 17

complete list of authors

• Rajan, Nithya||Maas, Stephan J

publication date

• August 2009

publisher

• Springer Nature  Publisher

published in

• Precision Agriculture  Journal

keywords

• Airborne Remote Sensing
• Ground Cover
• Perpendicular Vegetation Index (pvi)
• Soil Line

Digital Object Identifier (DOI)

• 10.1007/s11119-009-9116-2

start page

• 304

end page

• 318

volume

• 10

issue

• 4

URL

• http://dx.doi.org/10.1007/s11119-009-9116-2