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

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 mo...

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Bibliographic Details
Published in:BMC ecology 2019-04, Vol.19 (1), p.18-18, Article 18
Main Authors: Zhang, F, Zhou, G
Format: Article
Language:English
Subjects:
Agricultural production
Analysis
Canopy water content
China
Chlorophyll
Climate change
Corn
Crops
Dehydration
Drought
Forest & brush fires
Humans
Hyperspectral remote sensing
Irrigation
Leaf equivalent water thickness
Live fuel moisture content
Moisture content
Physiology
Plant Leaves
Predictions
Rain
Remote monitoring
Remote sensing
Scientific imaging
Seasonal variations
Seasons
Studies
Summer
Summer maize
Vegetation
Water
Water content
Water Purification
Water resources
Water shortages
Water stress
Water treatment
Zea mays
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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. 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 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 (CI ), red edge normalized ratio (NR ), and red-edge chlorophyll index (CI ) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. 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.
ISSN:1472-6785
1472-6785
DOI:10.1186/s12898-019-0233-0