Abstract
Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level (\({\overline{EWT}}\)) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CIgreen), red edge chlorophyll index (CIred edge), and the red edge normalized ratio (NRred edge) were most sensitive to the variations of CWC and \({\overline{EWT}}\). Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and \({\overline{EWT}}\). However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, \({\overline{EWT}}\) and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI).
Highlights
Vegetation water content serves as an important biophysical characteristic of terrestrial vegetation.Knowledge related to vegetation water conditions and how it varies can contribute to accurately detecting the physiological status of vegetation [1,2,3]
This study demonstrated that nearly half of the variation in canopy water content (CWC) explained by spectral information was derived from the variation in leaf area index (LAI)
In 2013, controlled-irrigation started on 24 July at the seven-leaf stage after observations on 23 July 2013 and relative soil water content (RSWC) gradients were observed from 29 July 2013
Summary
Knowledge related to vegetation water conditions and how it varies can contribute to accurately detecting the physiological status of vegetation [1,2,3]. It can provide useful information for making sound decisions related to agriculture irrigation and assist with drought monitoring assessment [2,4,5]. Two major approaches, physically-based radiative transfer models [5,6,10,24,25] and statistical models mainly including the first derivative reflectance spectra [6,7,25], spectral reflectance indices [5,7,10,24,26], continuum removed spectra analysis [6,10], and full spectrum methods [26] were used
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