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 (Td) and do not explicitly represent nighttime transpiration (Tn), 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 Td estimation. The better-performing daytime model was then improved by incorporating a nonlinear volumetric soil water content term (VWC2) identified through sensitivity analysis. A Tn model was further developed by incorporating longwave radiation, residual stomatal conductance, and CO2 inhibition effects into the SIF-Gc method. The improved Td model and the new Tn 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 Td estimation, with R2 of 0.72 and RMSE of 1.25 mm/d. After incorporating VWC2, the Td model improved consistently during 2023-2025, with the mean R2 increasing to 0.84 and RMSE decreasing by 18.67% on average. The Tn model also performed well, with R2 values of 0.81-0.84. The integrated 24 h transpiration framework achieved robust performance for total transpiration (Ttotal) during the 2023-2025 growing seasons, with R2 values of 0.82-0.90. Overall, explicitly separating and integrating Td and Tn improved the estimation of Ttotal and provided a practical framework for continuous 24 h transpiration estimation in irrigated maize systems in semi-arid regions.