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Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index,...

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Detalles Bibliográficos
Autores principales: Dong, Xuejun, Peng, Bin, Sieckenius, Shane, Raman, Rahul, Conley, Matthew M., Leskovar, Daniel I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380031/
https://www.ncbi.nlm.nih.gov/pubmed/34466291
http://dx.doi.org/10.7717/peerj.12005
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author Dong, Xuejun
Peng, Bin
Sieckenius, Shane
Raman, Rahul
Conley, Matthew M.
Leskovar, Daniel I.
author_facet Dong, Xuejun
Peng, Bin
Sieckenius, Shane
Raman, Rahul
Conley, Matthew M.
Leskovar, Daniel I.
author_sort Dong, Xuejun
collection PubMed
description Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. 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. These have limited the informed interpretation of NDVI data in agricultural applications. 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. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.
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spelling pubmed-83800312021-08-30 Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision Dong, Xuejun Peng, Bin Sieckenius, Shane Raman, Rahul Conley, Matthew M. Leskovar, Daniel I. PeerJ Agricultural Science Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. 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. These have limited the informed interpretation of NDVI data in agricultural applications. 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. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed. PeerJ Inc. 2021-08-18 /pmc/articles/PMC8380031/ /pubmed/34466291 http://dx.doi.org/10.7717/peerj.12005 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, made available under the Creative Commons Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/) . This work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Agricultural Science
Dong, Xuejun
Peng, Bin
Sieckenius, Shane
Raman, Rahul
Conley, Matthew M.
Leskovar, Daniel I.
Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title_full Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title_fullStr Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title_full_unstemmed Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title_short Leaf water potential of field crops estimated using NDVI in ground-based remote sensing—opportunities to increase prediction precision
title_sort leaf water potential of field crops estimated using ndvi in ground-based remote sensing—opportunities to increase prediction precision
topic Agricultural Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380031/
https://www.ncbi.nlm.nih.gov/pubmed/34466291
http://dx.doi.org/10.7717/peerj.12005
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