Rehman et al. (2026) Assessment of crop water stress under prevailing irrigation practice in Faisalabad, Pakistan using hyperspectral remote sensing

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-07-29
• Authors: Habib Ur Rehman, Rooh Ul Muqeem Alvi, Muhammad Waseem, Muhammad Qasim, Salman Ali Suhail, Muhammad Rashid, Farnaz
• DOI: 10.1016/j.agwat.2026.110660

Research Groups

• Department of Civil Engineering, The University of Lahore
• Department of Civil Engineering, University of Engineering and Technology
• Office of Sub Divisional Officer, Small Dams Subdivision No.III Fateh Jang
• Centre of Excellence in Water Resources Engineering, University of Engineering and Technology
• Department of Earth and Geoenvironmental Sciences, University of Bari Aldo Moro
• Faculty of Environmental Management, Prince of Songkla University

Short Summary

This study used hyperspectral remote sensing to assess crop water stress under prevailing irrigation practice in Faisalabad, Pakistan. The results showed that maize and spinach yields improved with water application until reaching an optimum level and thereafter improved at higher levels.

Objective

• To identify water stress in maize and spinach crops using hyperspectral reflectance data.
• To measure stage-specific associations between spectral vegetation indices (SVIs), soil moisture content, and crop yield.
• To analyze the current irrigation practices in perspective to under- and over-irrigation stresses.
• To propose a stage-specific irrigation program based on spectral thresholds.

Study Configuration

• Spatial Scale: Field-scale experiment conducted at Chak No. 494 G.B., Mamunkanjan, Faisalabad, Punjab Province, Pakistan (30◦51′47″ N, 72◦48′28″ E; elevation 184.5 m a.s.l.)
• Temporal Scale: Experiment was conducted from August 2019 to January 2020

Methodology and Data

• Models used: Hyperspectral spectroscopy with statistical modeling
• Data sources: Soil moisture, hyperspectral reflectance data, crop yield measurements

Main Results

• Spectral reflectance of maize and spinach leaves was different at varying water application rates.
• Leaves with 0% FC had the greatest reflectance, while 133% FC had the lowest reflectance in the VIS-NIR spectrum (325–1075 nm).
• Crop yields showed a classical bell-shaped response to water application, with maize reaching its highest yield at 100% FC and spinach at 66% FC.
• Under-irrigation reduced yield primarily by limiting stomatal conductance and photosynthesis, while over-irrigation reduced yield through waterlogging, root oxygen deficiency, and nutrient leaching.

Contributions

• This study fills the gap in field validation of hyperspectral indicators for crop water stress under real farmer-managed irrigation practice.
• The results provide a stage-specific irrigation program based on spectral thresholds to optimize irrigation strategies for maize and spinach crops.

Funding

• This research was funded by [project name], [program name], and [reference code].

Citation

@article{Rehman2026Assessment,
  author = {Rehman, Habib Ur and Alvi, Rooh Ul Muqeem and Waseem, Muhammad and Qasim, Muhammad and Suhail, Salman Ali and Rashid, Muhammad and Farnaz},
  title = {Assessment of crop water stress under prevailing irrigation practice in Faisalabad, Pakistan using hyperspectral remote sensing},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110660},
  url = {https://doi.org/10.1016/j.agwat.2026.110660}
}