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

Autor:	Dong X; Texas A&M AgriLife Research and Extension Center at Uvalde, Uvalde, TX, United States., Peng B; Yancheng Institute of Technology, Yancheng City, Jiangsu, China., Sieckenius S; Texas A&M AgriLife Research and Extension Center at Uvalde, Uvalde, TX, United States., Raman R; Texas A&M AgriLife Research and Extension Center at Uvalde, Uvalde, TX, United States.; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States., Conley MM; USDA-ARS, U.S. Arid-Land Agricultural Research Center, Maricopa, AZ, United States., Leskovar DI; Texas A&M AgriLife Research and Extension Center at Uvalde, Uvalde, TX, United States.
Jazyk:	angličtina
Zdroj:	PeerJ [PeerJ] 2021 Aug 18; Vol. 9, pp. e12005. Date of Electronic Publication: 2021 Aug 18 (Print Publication: 2021).
DOI:	10.7717/peerj.12005
Abstrakt:	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. Competing Interests: The authors declare that they have no competing interests.
Databáze:	MEDLINE
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