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Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize

Zhang, F., Zhou, G.
BMC ecology 2019 v.19 no.1 pp. 18
Zea mays, canopy, chlorophyll, corn, fuel moisture index, growing season, leaves, prediction, remote sensing, seasonal variation, vegetation, water content, water stress, China
BACKGROUND: 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. 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. RESULTS: Results 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. CONCLUSIONS: Compared 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.