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ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Abiotic Stress
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1520304
Combine Photosynthetic Characteristics and Leaf Hyperspectral Reflectance for Early Detection of Water Stress
Provisionally accepted
LinBao Li 1
Guiyun Huang 1* Jinhua Wu 1* Yunchao Yu 1* Guangxin Zhang 1* Yang Su 1* Xiongying Wang 2* Huiyuan Chen 1* Yeqing Wang 1* Di Wu 1*- 1 China Three Gorges Corporation, Beijing, China
- 2 Huazhong Agricultural University, Wuhan, Hubei Province, China
Advanced techniques capable of early and non-destructive detection of the impacts of water stress on trees and estimation of the underlying photosynthetic capacities on larger scale are necessary to meet the challenges of limiting plant growth and ecological protection caused by drought. We tested influence of continuous water stress on photosynthetic traits including Leaf Chlorophyll content (LCC) and Chlorophyll Fluorescence (ChlF) and combined hyperspectral reflectance as a high-throughput approach for early and non-destructive assessment of LCC and ChlF traits in Rhamnus leptophylla trees. LCC and ChlF parameters (NPQ, Fv'/Fm', ETR, ETRmax, Fm', qL, qP, Y(Ⅱ) were measured alongside leaf hyperspectral reflectance from Rhamnus leptophylla suffering from constant drought during water stress. Water stress caused NPQ, Fv'/Fm', ETRmax, Fm', qL, qP, Y( Ⅱ) and ETR continuous decline throughout the entire drought period. ChlF was more sensitive to drought monitoring than LCC. The original reflectance spectr a and hyperspectral vegetation indices (SVIs) showed a strong correlation with LCC and ChlF. Reflectance in 540-560nm and 750-1100nm and selected SVI such as Simple Ratio (SR)752/690 can track drought responses effectively before leaves showed drought symptoms. Multivariate Linear Regression (MLR) and three machine learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were employed to develop models for estimating LCC and ChlF parameters. RF provided the best estimation accuracy for LCC compared to MLR, KNN and SVM, achieving an R 2 value of 0.895 for all LCC samples. The canopy layer significantly influenced the estimation accuracy of LCC, with the middle layer yielding the highest R 2 value. RF also demonstrated superior performance compared to MLR, KNN and SVM for estimating NPQ, Fv'/Fm', ETRmax, Fm', qL, qP, Y(Ⅱ) and ETR, achieving R 2 value of 0.854 for NPQ, 0.610 for Fv'/Fm', 0.878 for ETRmax, 0.676 for Fm', 0.604 for qL, 0.731 for qP, 0.879 for Y(Ⅱ), and 0.740 for ETR. Our results indicate that photosynthetic traits combined hyperspectral reflectance can monitor the effect of drought on trees effectively with significant potential for monitoring drought over large areas.
Keywords: Chlorophyll Fluorescence, Hyperspectral reflectance, leaf chlorophyll content, machine learning algorithms, Rhamnus leptophylla, Water stress
Received: 01 Nov 2024; Accepted: 14 Feb 2025.
Copyright: © 2025 Li, Huang, Wu, Yu, Zhang, Su, Wang, Chen, Wang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Guiyun Huang, China Three Gorges Corporation, Beijing, China
Jinhua Wu, China Three Gorges Corporation, Beijing, China
Yunchao Yu, China Three Gorges Corporation, Beijing, China
Guangxin Zhang, China Three Gorges Corporation, Beijing, China
Yang Su, China Three Gorges Corporation, Beijing, China
Xiongying Wang, Huazhong Agricultural University, Wuhan, 430070, Hubei Province, China
Huiyuan Chen, China Three Gorges Corporation, Beijing, China
Yeqing Wang, China Three Gorges Corporation, Beijing, China
Di Wu, China Three Gorges Corporation, Beijing, China
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