Song et al. (2026) Decoupling of gross primary productivity and transpiration revealed through a daily remote sensing modelling approach in arid irrigated farmland

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-02-21
• Authors: Lisheng Song, Zhiwen Guan, Sinuo Tao, Michael J. Liddell, Gengle Zhao, Long Zhao, Shaomin Liu
• DOI: 10.1016/j.agrformet.2026.111085

Research Groups

• Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, Anhui Province, School of Geography and Tourism, Anhui Normal University, China
• Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, China
• Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, China
• College of Science and Engineering, James Cook University, Australia
• School of Resources and Environment, University of Electronic Science and Technology of China, China
• State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Sciences, Beijing Normal University, China

Short Summary

This study investigated the relationship between gross primary productivity (GPP) and transpiration (T) in arid, irrigated farmlands, revealing a weak coupling and significant decoupling where transpiration rates were disproportionately high relative to photosynthesis, especially during the middle to late growing season.

Objective

• To investigate the relationship between gross primary productivity (GPP) and transpiration (T) in arid, irrigated farmlands to understand water use efficiency and inform irrigation practices.

Study Configuration

• Spatial Scale: Arid, irrigated farmlands in the Heihe River Basin, China. Model validation performed in grassland, cropland, Tamarix shrub, and desert steppe environments.
• Temporal Scale: Daily data from 2000 to 2020 (21 years).

Methodology and Data

• Models used: Two-Source Energy Balance (TSEB-SM) model.
• Data sources:
  • Satellite data (thermal infrared and microwave soil moisture) for TSEB-SM model input.
  • Eddy covariance flux measurements for validation of evapotranspiration (ET).
  • Daily GPP and T data (T calculated by TSEB-SM).

Main Results

• A weak relationship was found between GPP and T in arid, irrigated farmlands of the Heihe River Basin.
• Increased T but steady GPP was observed, particularly in the middle to late growing season of irrigated crop areas, indicating low plant productivity per unit of water transpired.
• These findings align with the concept of irrigated crop decoupling, where plants maintain significantly higher transpiration rates than those associated with photosynthesis under intense heat stress and abundant water supply.
• The TSEB-SM model's estimates of daily evapotranspiration (ET) showed strong agreement with eddy covariance flux measurements:
  • Root Mean Square Error (RMSE) below 1.0 mm/day in grassland and cropland areas.
  • RMSE below 2.0 mm/day in Tamarix shrub and desert steppe environments.

Contributions

• Provides empirical evidence of GPP-T decoupling in arid, irrigated farmlands over a 21-year period using a daily remote sensing modeling approach.
• Highlights the inefficiency of water use in these agricultural systems, particularly under conditions of heat stress and abundant irrigation.
• Emphasizes the critical need for improved irrigation practices and increased real-time monitoring of water usage to achieve sustainable water management goals.
• Validates the TSEB-SM model for accurate daily ET estimation across diverse arid environments.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Song2026Decoupling,
  author = {Song, Lisheng and Guan, Zhiwen and Tao, Sinuo and Liddell, Michael J. and Zhao, Gengle and Zhao, Long and Liu, Shaomin},
  title = {Decoupling of gross primary productivity and transpiration revealed through a daily remote sensing modelling approach in arid irrigated farmland},
  journal = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111085},
  url = {https://doi.org/10.1016/j.agrformet.2026.111085}
}

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