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Assessing shaded-leaf effects on photochemical reflectance index (PRI) for water stress detection in winter wheat

Authors :
Shishi Liu
Xin Yang
Hang Su
Xifeng Ren
Yinuo Liu
Source :
Biogeosciences, Vol 16, Pp 2937-2947 (2019)
Publication Year :
2019
Publisher :
Copernicus Publications, 2019.

Abstract

The photochemical reflectance index (PRI) has emerged to be a pre-visual indicator of water stress. However, whether the varying shaded-leaf fractions, which may be caused by multiple view angles or the changing crop density in the field, affect the performance of PRI in detecting water stress of crops is still uncertain. This study evaluated the impact of the varying shaded-leaf fractions on estimating relative water content (RWC) across growth stages of winter wheat using seven formulations of PRI. Results demonstrated that for the control treatment the mean PRI of sunlit leaves was slightly higher than those of shaded leaves, but the difference between PRI of sunlit and shaded leaves increased as water resources became more limiting. Despite the difference between PRI of sunlit and shaded leaves, the significance of the linear relationship between RWC and most studied formulations of PRI did not show obvious variations with shadow fractions, except for the 100 % shaded-leaf condition. Among the studied formulations of PRI, PRI3 based on reflectance at 512 nm as the reference band provided the most accurate estimates of RWC with varying shaded-leaf fractions, except for the 100 % shaded-leaf condition. The slope and the intercept of linear regression models with PRI3 also showed minimized variations with shaded-leaf fractions. We then applied a uniform RWC prediction model to the data of varying shaded-leaf fractions and found that the accuracy of RWC predictions was not significantly affected in the mixture of sunlit and shaded leaves. However, RWC estimated with PRI of the 100 % shaded-leaf condition had the highest root mean square error (RMSE), implying that PRI of the pure shaded leaves may yield inaccurate estimates of plant water status.

Details

Language :
English
ISSN :
17264189 and 17264170
Volume :
16
Database :
OpenAIRE
Journal :
Biogeosciences
Accession number :
edsair.doi.dedup.....52d63e688a5cf8b794631f0ace8c68e9