Optimization of Cotton Field Irrigation Scheduling Using the AquaCrop Model Assimilated with UAV Remote Sensing and Particle Swarm Optimization

Fangyin Wang, Qiuping Fu (), Ming Hong, Wenzheng Tang (), Lijun Su, Dongdong Zhu and Quanjiu Wang
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Fangyin Wang: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumgi 830052, China
Qiuping Fu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumgi 830052, China
Ming Hong: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumgi 830052, China
Wenzheng Tang: College of Water Engineering, Yellow River Conservancy Technical University, Kaifeng 475000, China
Lijun Su: School of Science, Xi’an University of Technology, Xi’an 710054, China
Dongdong Zhu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumgi 830052, China
Quanjiu Wang: Xinjiang Future Irrigation District Engineering Technology Research Center, Wensu 843100, China

Agriculture, 2025, vol. 15, issue 17, 1-31

Abstract: In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient ( CGC ), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover ( CC ) simulation, while the crop coefficient at full canopy (KCTRX), water productivity ( WP ), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R 2 values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R 2 values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China.

Keywords: cotton; UAV remote sensing; AquaCrop model; parameter assimilation; irrigation scheduling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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