Unverified paper record
Enhancing AquaCrop-OSPy yield predictions with UAV-based remote sensing data: a case study on broccoli
Computers and Electronics in Agriculture. · 1 Mar 2026
Abstract
Efficient irrigation of horticultural crops under increasing water scarcity requires crop models that exploit high–resolution remote–sensing (RS) data. This study evaluated how unmanned aerial vehicle (UAV) multispectral and thermal observations improved AquaCrop-OSPy simulations of canopy cover (CC), actual evapotranspiration (ETₐ) and yield for irrigated broccoli under Mediterranean conditions. Broccoli was grown for two seasons in a 0.2 ha field in eastern Spain under two irrigation strategies: decision–support Irrigation Advisor (IA) versus farmer practice. A global sensitivity analysis (GSA) and two–stage calibration against Season 1 CC and yield identified canopy growth (CGC), harvest index (HIₒ) and transpiration phenology (GDDᵤₚ) as dominant controls; the calibrated model was validated in Season 2. UAV imagery provided CC via supervised classification and ETₐ via a two–source energy balance model (pyTSEB), which were assimilated into AquaCrop-OSPy on three dates using a hybrid observed–simulated scheme. Compared with lysimeter measurements, pyTSEB reproduced ETₐ with root–mean–square error (RMSE) 0.39 mm d⁻¹ and Nash–Sutcliffe efficiency (NSE) 0.93, whereas baseline AquaCrop-OSPy showed RMSE 1.24 mm d⁻¹ and NSE 0.27. Without assimilation, AquaCrop-OSPy reproduced mean yield but not subplot variability (RMSE 1.67 t ha⁻¹). Assimilating CC reduced yield RMSE by 8.9 %, ETₐ alone gave smaller gains, and joint CC + ETₐ assimilation achieved the lowest RMSE (1.47 t ha⁻¹, 11.9 % reduction). Across seasons, IA applied 20.6 % more water than farmer practice with no consistent yield or water–productivity benefits. These results, obtained within the limitations of this study, indicate that UAV-derived CC, complemented by ETₐ, modestly improves AquaCrop-OSPy yield predictions. Nevertheless, they should be interpreted as indicative rather than definitive and motivate further evaluations.
Plant phenotyping relevance
UAV画像からブロッコリーの canopy cover と実蒸発散量を推定し、作物モデルへの同化性能を検証することが研究の中心であり、植物形質・状態の取得と技術評価に該当する。
abstractUAV imagery provided CC via supervised classification and ETₐ via a two–source energy balance model (pyTSEB), which were assimilated into AquaCrop-OSPy on three dates using a hybrid observed–simulated scheme.
abstractCompared with lysimeter measurements, pyTSEB reproduced ETₐ with root–mean–square error (RMSE) 0.39 mm d⁻¹ and Nash–Sutcliffe efficiency (NSE) 0.93
abstractAssimilating CC reduced yield RMSE by 8.9 %, ETₐ alone gave smaller gains, and joint CC + ETₐ assimilation achieved the lowest RMSE
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