ArticleFrontiers in plant science2024
Water content estimation of conifer needles using leaf-level hyperspectral data.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Antioxidant and metabolic responses of Linum album cell culture to polyethylene glycol-induced drought stress.BMC plant biology · 2026Article
- Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning.Scientific reports · 2026Article
- Effect of Pyroligneous Acid on Needle Retention and Certain Stress-Related Phytochemicals in Balsam Fir (Plants (Basel, Switzerland) · 2026Article
- Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning.Scientific reports · 2025Article
- Mind the leaf anatomy while taking ground truth with portable chlorophyll meters.Scientific reports · 2025Article
- Estimation of the water content of needles under stress byFrontiers in plant science · 2025Article
- Adaptive growth strategies ofFrontiers in plant science · 2024Article
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Abstract
Water is a crucial component for plant growth and survival. Accurately estimating and simulating plant water content can help us promptly monitor the physiological status and stress response of vegetation. In this study, we constructed water loss curves for three types of conifers with morphologically different needles, then evaluated the applicability of 12 commonly used water indices, and finally explored leaf water content estimation from hyperspectral data for needles with various morphology. The results showed that the rate of water loss of Olgan larch is approximately 8 times higher than that of Chinese fir pine and 21 times that of Korean pine. The reflectance changes were most significant in the near infrared region (NIR, 780-1300 nm) and the short-wave infrared region (SWIR, 1300-2500 nm). The water sensitive bands for conifer needles were mainly concentrated in the SWIR region. The water indices were suitable for estimating the water content of a single type of conifer needles. The partial least squares regression (PLSR) model is effective for the water content estimation of all three morphologies of conifer needles, demonstrating that the hyperspectral PLSR model is a promising tool for estimating needles water content.
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