## 1 Introduction
[6] showed that the relationship between canopy SIF and GPP was asymptotic and scaled with environmental variables and the temporal resolution for crop fields. Although much understanding of the response of GPP to SIF has been gained from numerous SIF-based GPP models at the leaf and canopy scales [14; 32; 40; 41; 42], there is general agreement among these models that the accuracy of GPP estimates is affected by the light conditions and environmental variables. In addition, different models may have different sensitivities to the timescales and environmental variables used for predicting canopy photosynthesis production. Generally, CI has been used as an indicator of solar light conditions that can distinguish different weather conditions, such as sunny days and cloudy days [30; 43]. Several studies have demonstrated that weather conditions can influence the canopy GPP and SIF. For example, [PERSON] et al. [44] observed that an understory herb in a mixed spruce stand had a stronger ability to gain carbon on representative cloudy days than clear days. [PERSON] et al. [45] found that the effect of different environmental factors on ecosystem carbon sequestration was complex, especially under cloudy conditions. Diffuse and direct radiation differed in the way they were transmitted though the canopy, and the diffuse radiation had a much lower tendency to cause canopy photosynthetic saturation in the case of forest, tall grass, and winter wheat ecosystems [46]. [PERSON] et al. [40] also showed that the relationship between GPP and SIF differed depending on the light conditions. Previous studies have demonstrated that CI is usually related to other environmental parameters, such as the temperature, humidity, and precipitation. Recently, [PERSON] et al. [31] found that the ratio of GPP to SIF generally increased along with the relative humidity (RH) and confirmed that incorporating the relative humidity and growth stage into multiple regression analyses led to improvements in estimates of GPP for a rice paddy. However, irrespective of whether a linear model or non-linear model is used for SIF-based GPP estimation, the effects of CI are still unknown, which is important for an accurate assessment of GPP. On the other hand, although current studies have found the absorbed photosynthetically active radiation (APAR) is the dominant factor in the SIF-GPP relationship, [PERSON] et al. [35] recently noted that SIF-GPP relationships were not only driven by APAR, but were also strongly correlated with the leaf-level biochemistry and canopy structure at diurnal and seasonal scales in temperate deciduous forests. Therefore, future studies should pay more attention to the effects of the plant physiology, canopy structure, and environmental conditions on the correlation between SIF and GPP across the growing season. Overall, the strong link between SIF and photosynthesis has permitted the development of a new approach for accurately estimating GPP at different spatial and temporal scales [47]. In this study, we deployed an automated tower-based SIF system in a maize field to collect continuous observations of far-red SIF, which were then paired with measurements made at an existing EC flux tower so that carbon exchange data were collected simultaneously. The acquired data covered the growing seasons of 2017 and 2018. The objectives were: (1) to investigate the ability of far-red SIF to track maize GPP at diurnal and seasonal scales, (2) to explore the impact of the environmental conditions on the SIF-GPP relationship, and (3) to improve the estimation of GPP by integrating SIF measurements and the clearness index.