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.
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Acknowledgments
The authors thank Texas Alliance for Water Conservation (TAWC) for funding the study. The authors thank Dr. Wenxuan Guo for providing technical assistance during aerial flight missions. The authors also thank Mr. Shyam. S. Nair for his help in collecting field data.
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Rajan, N., Maas, S.J. Mapping crop ground cover using airborne multispectral digital imagery. Precision Agric 10, 304–318 (2009). https://doi.org/10.1007/s11119-009-9116-2
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DOI: https://doi.org/10.1007/s11119-009-9116-2