known as the maximum carboxylation rate (V\({}_{\text{cmax}}\)), which characterizes the potential photosynthetic capacity of leaves [11; 13]. Consequently, the accurate estimation of the V\({}_{\text{cmax}}\) is paramount for enhancing the accuracy and performance of TBMs [14]. V\({}_{\text{cmax}}\) fluctuates abruptly during the crop growth period; thus, taking the variation in V\({}_{\text{cmax}}\) into account is crucial for accurately calculating GPP in models [15; 16; 17]. However, V\({}_{\text{cmax}}\) cannot be directly measured, and it must be inferred from leaf gas exchange measurement, which is time-consuming, resulting in limited data availability across a wide range of conditions [18]. In most TBMs, V\({}_{\text{cmax}}\) is assumed to be a fixed value (normalized to 25 \({}^{\circ}\)C, V\({}_{\text{m25}}\)) based on plant functional type, disregarding temporal and spatial variations in V\({}_{\text{m25}}\)[7; 19; 20; 21; 22]. This assumption inevitably introduces significant bias in simulated photosynthesis, particularly in regions characterized by substantial seasonal fluctuations [15; 23]. To minimize the bias, temporal variations in V\({}_{\text{m25}}\) are modeled by establishing relationships between V\({}_{\text{m25}}\) and more readily available plant traits [18; 24]. Some studies have inverted V\({}_{\text{m25}}\) using eddy covariance measurements and remote sensing data [25; 26; 27; 28; 29; 30]. [PERSON] used the ensemble Kalman filter (EnKF) to obtain the temporal variation in the V\({}_{\text{m25}}\) of maize [17]. [PERSON] and [PERSON] inverted the time series of V\({}_{\text{m25}}\) by coupling Boreal Ecosystem Productivity Simulator (BEPS) and light response curve (LRC) based on eddy covariance observations in flux sites [31; 32]. The results indicated that this approach can effectively optimize V\({}_{\text{m25}}\), but it has only been applied to C3 plants, and its applicability to C4 plants has not been studied yet [31; 32; 33]. Despite technological advances in deriving V\({}_{\text{m25}}\), modeling V\({}_{\text{m25}}\) through plant traits remains challenging. Advances in remote sensing and hyperspectral imaging technology have proven useful for estimating photosynthetic capacity across large spatial and temporal scales [18; 34; 35; 36; 37; 38]. The strong correlation between the leaf nitrogen content (N\({}_{\text{Leaf}}\)) and V\({}_{\text{m25}}\) has garnered significant attention [39; 40; 41]. However, obtaining accurate N\({}_{\text{Leaf}}\) through remote sensing data remains a challenge, which impedes the use of leaf nitrogen to parameterize V\({}_{\text{m25}}\)[8; 24]. Chlorophyll plays a crucial role in photosynthesis by capturing photons and providing the biochemical energy necessary for carbon fixation reactions [42]. Since N\({}_{\text{Leaf}}\) includes both photosynthetic and non-photosynthetic components, some researchers argue that leaf chlorophyll content (Chll) is a more accurate indicator of V\({}_{\text{m25}}\) than N\({}_{\text{Leaf}}\) content [24; 43]. [PERSON] summarized the semi-empirical relationship between Chl and V\({}_{\text{m25}}\)[19]. By parameterizing this relationship, it has been successfully applied to a variety of crops [18; 44; 45]. The practical limitation of using Chll to parameterize V\({}_{\text{m25}}\) at large spatial scales has been the lack of accurate remote sensing Chll products at regional or global scales. The Sentinel-2A satellite, launched in 2015, and the Sentinel-2B satellite, launched in 2017, carry multispectral imagers (MSIs) with red-edge bands sensitive to Chll and offer high temporal and spatial resolution. However, estimating Chll relies on complex models that typically require ground validation data for calibration [24; 46]. The availability of the satellite observations of sun-induced fluorescence (SIF) offers a new perspective for monitoring crop V\({}_{\text{m25}}\)[19; 20]. Chlorophyll fluorescence is widely regarded as a direct proxy for electron transport and, consequently, photosynthesis [47; 48].