Adedeji et al. (2025) Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-12
• Authors: Oluwatola Adedeji, Yazhou Sun, Sanai Li, Wenxuan Guo
• DOI: 10.3390/rs17244018

Research Groups

• Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA
• Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Lubbock, TX, USA

Short Summary

This study evaluated the performance of UAS-derived Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) across cotton growth stages and examined how spatial resolution influences stress detection and yield prediction. It found that WDI outperformed CWSI, and a 0.5 m spatial resolution provided the optimal balance between accuracy and computational efficiency for assessing cotton water stress and predicting yield.

Objective

• To evaluate the application of Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) derived from Unmanned Aerial System (UAS) images in detecting crop water stress at different cotton growth stages.
• To examine the effect of spatial resolution of UAS-derived WDI and CWSI on assessing cotton water stress and predicting yield.

Study Configuration

• Spatial Scale: Field experiments conducted in Lubbock County, Texas, across 96 plots, each approximately 4 m wide and 8 m long. UAS imagery was acquired at an initial spatial resolution of 0.03 m (multispectral) and 0.05 m (thermal) from a flight altitude of 40 m, then resampled to 0.1 m, 0.2 m, 0.3 m, 0.4 m, 0.5 m, 1.0 m, 2.0 m, 3.0 m, and 4.0 m.
• Temporal Scale: Two cotton growing seasons (2021 and 2022). Image acquisitions were performed at three key growth stages (early, mid, late season) in each year: 60, 76, and 101 days after planting (DAP) in 2021; and 58, 78, and 119 DAP in 2022.

Methodology and Data

• Models used:
  • Water Deficit Index (WDI) based on the Vegetation Index/Temperature (VIT) trapezoid model.
  • Crop Water Stress Index (CWSI) based on canopy, wet, and dry canopy temperatures.
  • FAO-56 Penman-MonteMonteith equation for calculating reference evapotranspiration (ET₀) and aerodynamic resistance (rₐ).
  • Linear mixed-effects model (LMM) for statistical analysis of WDI and CWSI data.
  • Linear regression models for cotton yield prediction.
  • Tukey's HSD post hoc test for pairwise comparisons.
• Data sources:
  • UAS Imagery:
    • DJI Matrice 600 Pro platform.
    • MicaSense RedEdge-MX multispectral camera (blue, green, red, red edge, near-infrared bands).
    • DJI Zenmuse XT Radiometric thermal sensor (7.5 µm to 13.5 µm).
  • Ground Data:
    • Meteorological data (air temperature, relative humidity, wind speed, rainfall, solar radiation) from an on-site weather station.
    • Cotton lint yield data collected for each plot at the end of the season.
    • Reference temperature measurements (Apogee MI-220) for calibration.
    • Manual canopy height measurements.
  • Software: Pix4DMapper, Agisoft PhotoScan Professional, ArcGIS Pro, Python (v3.10) with statsmodels and scikit-learn packages.

Main Results

• The Water Deficit Index (WDI) consistently outperformed the Crop Water Stress Index (CWSI) in distinguishing water stress levels across cotton growth stages, particularly during early growth stages with partial canopy cover.
• A spatial resolution of 0.5 m was identified as the optimal balance, providing high accuracy in water stress detection and yield prediction while maintaining computational efficiency.
• Finer resolutions (0.1 m to 0.4 m) offered more detail but significantly increased data volume, flight duration, and processing time, making them less practical for routine assessments.
• Coarser resolutions (≥1 m) led to reduced accuracy in water stress assessment and yield prediction due to spatial averaging and plot-mixing effects.
• WDI showed higher coefficients of determination (R²) and lower Root Mean Square Error (RMSE) for cotton yield prediction across all tested spatial resolutions compared to CWSI (e.g., in 2021, WDI R² ~0.55, RMSE ~145 kg/ha at fine resolutions; CWSI R² ~0.49, RMSE ~160 kg/ha at 4.0 m).
• Cotton lint yield was significantly affected by irrigation rates, with higher irrigation (90% ET) consistently resulting in substantially higher yields (e.g., median yield of ~1350 kg/ha for 90% ET in 2021 vs. ~1000 kg/ha for 30% ET).

Contributions

• Provided a comprehensive evaluation of the impact of spatial resolution (from 0.1 m to 4.0 m) on UAS-derived water stress indices (WDI and CWSI) for cotton.
• Identified an optimal spatial resolution (0.5 m) for cotton water stress assessment and yield prediction, offering practical guidance for UAS deployment in precision agriculture.
• Demonstrated the superior performance of WDI over CWSI, especially in early growth stages, highlighting its robustness for precision irrigation management.
• Offered valuable insights for optimizing UAS flight altitude and sensor configurations to achieve efficient, scalable, and precise cotton water stress monitoring and yield prediction.

Funding

• USDA NIFA (Award No. 2023-70001-40993)
• USDA NIFA and Cotton Board (Award No. 2022-67013-36992)
• USDA NIFA HATCH (Award No. 9898)
• USDA ARS (OAP 58-3090-1-006)
• Cotton Incorporated (Award No. 17-012)

Citation

@article{Adedeji2025Effects,
  author = {Adedeji, Oluwatola and Sun, Yazhou and Li, Sanai and Guo, Wenxuan},
  title = {Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17244018},
  url = {https://doi.org/10.3390/rs17244018}
}