Table 1.
Published vegetation indices used in this study for water status estimation.
Fig 1.
Canopy spectral reflectance (raw data) at various plant water status in winter wheat.
Different colors indicate the mean spectrum of various gradients of water metrics. (A) equivalent water thickness (EWT), (B) canopy water content (CWC), (C)leaf water content (LWC) and (D) plant water content (PWC).
Fig 2.
First derivative reflectance at various plant water status in winter wheat.
Different color represents gradients of water metrics. (A) equivalent water thickness (EWT), (B) canopy water content (CWC), (C)leaf water content (LWC) and (D) plant water content (PWC).
Fig 3.
Correlation between water metrics and raw reflectance and between water metrics and the first derivative reflectance in winter wheat (n = 120).
(A) equivalent water thickness (EWT), (B) canopy water content (CWC), (C) leaf water content (LWC) and (D) plant water content (PWC). The horizontal dotted lines represent the correlation coefficient threshold values at the 0.01 probability level.
Fig 4.
Coefficient of determination (R2) between plant water status and spectral indices using two bands of the raw reflectance in winter wheat.
Fig 5.
Coefficient of determination (R2) between plant water status and spectral indices using two bands of the first derivative reflectance in winter wheat.
Table 2.
Quantitative models of water metrics (y) to select spectral index (x) in winter wheat.
Fig 6.
Scatter diagrams between the measured and estimated water metrics from the models with the optimal spectral index.
(A) canopy water content (CWC), (B) leaf water content (LWC) and (C) plant water content (PWC). The 1:1 line is marked with a dotted line.
Table 3.
Correlation coefficients between vegetation indices and water metrics (n = 120).
Table 4.
The performance of the calibration models based on the published vegetation indices.