Abstract
BackgroundVegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013–2015 in North Plain China.ResultsResults showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CIgreen), red edge normalized ratio (NRred edge), and red-edge chlorophyll index (CIred edge) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT.ConclusionsCompared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.
Highlights
Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress
We compared the responses of canopy water content (CWC), equivalent water thickness (EWT), and live fuel moisture content (LFMC) to water stress and their estimations using spectral vegetation indices (VIs)
Responses of CWC, EWT, and LFMC to water stress Taking the data from the experiment in 2013 as an example, CWC, EWT, LFMC, and relative soil water content (RSWC) in response to different water treatments and their seasonal variation
Summary
Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Drought is one of the most important impacts of global climate change on terrestrial ecosystems It is a major environmental abiotic stress factor that currently reduces crop yield worldwide [1]. Remote estimation of vegetation water content can provide important implications on vegetation physiological status detection [7, 12, 17,18,19,20], agricultural irrigation decision [10, 12, 13], and drought assessment [21, 22]. Remote sensing techniques can be used to effectively monitor and diagnose vegetation water conditions, accurately reflect physiological status of vegetation under water stress, rapidly recognize drought, and immediately adopt irrigation measures [10, 12, 13, 22, 23]
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