Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery

作者：Zhaokui Li, Hongli Li, Xue‐Wei Gong, Heng-Fang Wang, Guang‐You Hao · 发表于：Plant Methods · 年份：2024 · DOI：10.1186/s13007-024-01312-1 · 被引用次数：10 · 研究领域：Plant Water Relations and Carbon Dynamics、Remote Sensing in Agriculture、Leaf Properties and Growth Measurement

Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var. pyramidalis , a species widely employed for constructing farmland shelterbelts in water-limited regions of northern China but notably susceptible to drought. These samples were dehydrated to varying degrees to generate concurrent LWC measurements and hyperspectral images, enabling the development of narrow-band and multivariate spectral prediction models for LWC estimation. Two visible-spectrum narrow-band indices identified, the single-band index ( R 627 ) and the band subtraction index ( R 437 − R 444 ), demonstrated a strong correlation with LWC. Despite certain influences of variable preprocessing and selection on multivariate model performance, most models exhibited robust predictive accuracy for LWC. The FDRL-UVE-PLSR combination emerged as the optimal multivariate model, with R 2 values reaching 0.9925 and 0.9853 and RMSE values below 0.0124 and 0.0264 for the calibration and validation datasets, respectively. Using this optimal model, along with localized spectral smoothing, moisture distribution across ...