Studies have demonstrated a strong connection between SIF and V\({}_{\text{m25}}\), indicating that SIF could be helpful for improving the accuracy of V\({}_{\text{m25}}\) estimations at large spatial scales [42; 49; 50]. However, although SIF is effective and remote sensing products with 500-5000 m resolution are available, such as CSIF and GOSIF, the resolution of current SIF products is still relatively low compared to other vegetation parameter products, which can achieve a resolution of 30 m or higher [51]. The vegetation canopy structure directly influences various physical and biological processes, such as radiative transfer and photosynthesis [52]. Thus, in recent years, vegetation indices (VIs) have been employed to empirically estimate V\({}_{\text{m25}}\)[53]. [PERSON], et al. [54] divided the growth period into two stages and established distinct relationships between VIs and canopy-level V\({}_{\text{m25}}\) for each stage. However, [PERSON] highlighted that the relationship between traditional VIs and V\({}_{\text{m25}}\) was not universal across different sites [55]. LAI is a VI commonly used to characterize seasonal changes in V\({}_{\text{m25}}\)[56]. However, it is commonly observed that photosynthesis peaks earlier than the canopy structure indices [57]. Leaf age has been found to be well correlated with V\({}_{\rm m25}\) and substantial evidence indicates biochemical differences between young and old leaves [58; 59]. Recent studies have demonstrated that leaf age plays a crucial role in determining photosynthetic rates [60; 61; 62], with V\({}_{\rm m25}\) exhibiting notable changes with the aging of leaves [63; 64; 65; 8; 16]. Generally, it is observed that the V\({}_{\rm m25}\) of newly mature leaves tends to be higher compared to that of younger and older leaves [66]. As leaves undergo senescence, Rubisco gradually becomes inactivated, the electron transport rate decreases, and enzymes are deactivated. Consequently, both the rates of photosynthesis and respiration decline with leaf aging [58; 59; 67]. It is worth noting that most of the research on the relationship between V\({}_{\rm m25}\) and leaf age has focused on trees [24; 66], and there has been limited investigation into this relationship in crops [58; 59; 68]. [PERSON] and [PERSON] analyzed the seasonal changes in V\({}_{\rm m25}\) for maize and sunflower through gas exchange experiments [6]. The study revealed that the V\({}_{\rm m25}\) of maize decreases in a nearly linear trend from the mid-vegetative stage to the late senescence stage. For sunflowers, V\({}_{\rm m25}\) remained relatively stable from the late vegetative stage to the early reproductive stage, then significantly decreased in the late reproductive stage. [PERSON] found that rice V\({}_{\rm m25}\) exhibited a trend similar to leaf age under different experimental conditions and emphasized that a general formula for V\({}_{\rm m25}\) changes with leaf age has not yet been established [65]. Despite the well-known, strong correlation between leaf age and V\({}_{\rm m25}\) in crops, to our knowledge, this relationship has not yet been quantified. Therefore, the objectives of our study are (1) to derive time-continuous V\({}_{\rm m25}\) for maize at five sites by considering the photosynthesis process of C4 plants; (2) to calibrate the maximum value of V\({}_{\rm m25}\) (i.e., PeakV\({}_{\rm m25}\)) during the growing season of maize based on the inverted V\({}_{\rm m25}\); and (3) to propose a Two-stage linear model that leverages leaf age to improve the accuracy of V\({}_{\rm m25}\) and GPP estimation.