A unified SIF-based framework integrating daytime and nighttime transpiration for irrigated maize in semi-arid regions

作者：Lingxin Bu, Mengting Qin, Qian Jialin, Mengyang Zhao, Chuansong Zhang, Bo Zhao, Yuhang Song, Kepeng Feng · 发表于：Agricultural Water Management · 年份：2026 · DOI：10.1016/j.agwat.2026.110385 · 研究领域：Plant Water Relations and Carbon Dynamics、Climate change impacts on agriculture、Irrigation Practices and Water Management

Accurate estimation of crop transpiration is important for understanding water use in irrigated agroecosystems. However, most existing solar-induced chlorophyll fluorescence (SIF)-based transpiration models focus on daytime transpiration ( T d ) and do not explicitly represent nighttime transpiration ( T n ), limiting their ability to estimate continuous 24 h crop water use. This study developed an integrated 24 h SIF-based transpiration framework for estimating maize transpiration across daytime and nighttime periods in a semi-arid irrigation district. Using the 2023 eddy covariance fluxes and meteorological observations, the SIF–Gc and SIF–GPP approaches were first evaluated for T d estimation. The better-performing daytime model was then improved by incorporating a nonlinear volumetric soil water content term ( VWC 2 ) identified through sensitivity analysis. A T n model was further developed by incorporating longwave radiation, residual stomatal conductance, and CO 2 inhibition effects into the SIF–Gc method. The improved T d model and the new T n model were then combined into an integrated 24 h transpiration framework, with 2023 used for calibration and 2024–2025 for validation. The results indicated that the SIF–Gc approach performed better than the SIF–GPP approach for T d estimation, with R 2 of 0.72 and RMSE of 1.25 mm/d. After incorporating VWC 2 , the T d model improved consistently during 2023–2025, with the mean R 2 increasing to 0.84 and RMSE decreasing by 18.67...