Li et al. (2026) Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-09-19
• Authors: Qing Li, Dalei Hao, Jan Pisek, Zicheng Ji, Yanan Wei, Youngryel Ryu, Jiarui Xu, Yangmin Feng, Shaozhong Kang, Yelu Zeng
• DOI: 10.1016/j.rse.2026.115674

Research Groups

• College of Land Science and Technology, China Agricultural University
• State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University
• Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs
• School of Resources and Environmental Sciences, Wuhan University
• University of Tartu, Tartu Observatory
• Department of Landscape Architecture and Rural Systems Engineering, Seoul National University
• School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture

Short Summary

The study developed a UAV-based analytical workflow called CAPLA to monitor the diurnal dynamics of cotton leaf inclination angle (LIA) under combined water and salinity stress. The results demonstrate that irrigation, salinity, and time significantly influence LIA, highlighting the necessity of dynamic structural monitoring over static canopy assumptions.

Objective

• To develop a high-resolution method for detecting diurnal leaf inclination angle (LIA) dynamics and to evaluate how combined water deficit and salinity stress affect these geometric adjustments in cotton canopies.

Study Configuration

• Spatial Scale: Plot level (cotton canopy)
• Temporal Scale: Diurnal (five observation times per day)

Methodology and Data

• Models used: Constraint-Assisted Point cloud fusion for Leaf scale Analysis (CAPLA), which integrates deep learning (2D semantic masks) with Structure from Motion (SfM).
• Data sources: UAV photogrammetry and ground-based plot-level mean leaf angle (MLA) observations.

Main Results

• Validation: The CAPLA workflow achieved high accuracy in estimating MLA, with an $R^2$ of 0.89 and an $\text{RMSE}$ of $0.9^\circ$.
• Stress Effects: Irrigation, salinity, and observation time had significant individual effects on MLA.
• Interactions: A significant interaction was found between irrigation and time ($P = 0.0109$), indicating that diurnal MLA trajectories vary by irrigation level.
• Non-significant Results: No significant interactions were found for irrigation $\times$ salinity ($P = 0.8800$) or irrigation $\times$ salinity $\times$ time ($P = 0.9086$).

Contributions

• Introduces CAPLA, a novel UAV analytical workflow that mitigates motion-induced artifacts in 3D reconstruction using deep learning constraints.
• Provides empirical evidence of the complex structural plasticity of cotton canopies under combined abiotic stresses, challenging the use of static canopy architecture in ecosystem models and precision agriculture.

Funding

• Not specified in the provided text.

Citation

@article{Li2026Detecting,
  author = {Li, Qing and Hao, Dalei and Pisek, Jan and Ji, Zicheng and Wei, Yanan and Ryu, Youngryel and Xu, Jiarui and Feng, Yangmin and Kang, Shaozhong and Zeng, Yelu},
  title = {Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115674},
  url = {https://doi.org/10.1016/j.rse.2026.115674}
}