We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. These have limited the informed interpretation of NDVI data in agricultural applications. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. Leaf water potential of field crops estimated using NDVI in ground-based remote sensing-opportunities to increase prediction precision. Cite this article Dong X, Peng B, Sieckenius S, Raman R, Conley MM, Leskovar DI. This work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. Arid-Land Agricultural Research Center, Maricopa, AZ, United States DOI 10.7717/peerj.12005 Published Accepted Received Academic Editor Le Yu Subject Areas Agricultural Science, Ecology, Plant Science, Spatial and Geographic Information Science Keywords Drought stress, Leaf water status, Statistical resampling, Multispectral sensor, Ordinary least-squares, Proximal sensing, Weighted least-squares, Measurement errors, Diurnal change, Model coefficients Licence This is an open access article, free of all copyright, made available under the Creative Commons Public Domain Dedication.
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