Ding et al. (2025) Estimating cropland evapotranspiration based on remote sensing models: A global meta-analysis

Identification

• Journal: European Journal of Agronomy
• Year: 2025
• Date: 2025-07-05
• Authors: Jiayi Ding, Jieling Yin, Bernard A. Engel, Xinyu Wei, Bing Wang, Shikun Sun, Altyn Shayakhmetova, Fariza Mukanova, Aldiyar Bakirov, Ainura Balakhmetova, Yubao Wang
• DOI: 10.1016/j.eja.2025.127758

Research Groups

• Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest A&F University, China
• Institute of Water Saving Agriculture in Arid regions of China, Northwest A&F University, China
• Department of Agricultural and Biological Engineering, Purdue University, USA
• Shaanxi Weihe Ecological Zone Protection Center, China
• Manash Kozybayev North Kazakhstan University, Kazakhstan

Short Summary

This study presents a global meta-analysis of 690 published articles to evaluate the performance, accuracy, and applicability of various remote sensing (RS) models used to estimate cropland evapotranspiration (ETc).

Objective

• To quantitatively assess the factors influencing the accuracy of RS-based ETc models and determine the specific applicability of different model types across various environmental conditions.

Study Configuration

• Spatial Scale: Global
• Temporal Scale: Multi-decadal (based on the analysis of published literature)

Methodology and Data

• Models used: Remote sensing (RS) models, specifically categorized into single-source, two-source, Penman-Monteith (PM), Priestley-Taylor (PT), empirical, and semi-empirical models.
• Data sources: Meta-analysis of 690 published scientific articles.

Main Results

• Model Trends: While the use of RS models for ETc estimation has increased rapidly, model accuracy has not exhibited a simple linear upward trend over time.
• ETc Trends: 62% of the analyzed studies reported an increasing trend in ETc over time, driven by changes in temperature and precipitation.
• Model Performance: PM and PT models show high accuracy under homogeneous crop cover; empirical and semi-empirical models demonstrate the greatest stability, although they remain highly dependent on input data.
• Accuracy Drivers: The inclusion of soil moisture data significantly improves model accuracy, with the most pronounced effect observed in irrigated semi-arid areas.

Contributions

• The study provides the first systematic quantitative integration of global findings on RS-based ETc estimation.
• It offers critical guidance for the selection of ETc models based on environmental factors and data availability, supporting precision agriculture and efficient water resource allocation.

Funding

• Not specified in the provided text.

Citation

@article{Ding2025Estimating,
  author = {Ding, Jiayi and Yin, Jieling and Engel, Bernard A. and Wei, Xinyu and Wang, Bing and Sun, Shikun and Shayakhmetova, Altyn and Mukanova, Fariza and Bakirov, Aldiyar and Balakhmetova, Ainura and Wang, Yubao},
  title = {Estimating cropland evapotranspiration based on remote sensing models: A global meta-analysis},
  journal = {European Journal of Agronomy},
  year = {2025},
  doi = {10.1016/j.eja.2025.127758},
  url = {https://doi.org/10.1016/j.eja.2025.127758}
}