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Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( ${\varepsilon _{{\text{max}}}}$ ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of ${\varepsilon _{{\text{max}}}}$ , and (b) separately re-parameterizing ${\varepsilon _{{\text{max}}}}$ in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art ${\varepsilon _{{\text{max}}}}$ –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three ${\varepsilon _{{\text{max}}}}$ –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three ${\varepsilon _{{\text{max}}}}$ –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m ^−2 d ^−1 ) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR _v ) were used to re-parameterize the ${\varepsilon _{{\text{max}}}}$ , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of ${\varepsilon _{{\text{max}}}}$ and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.