###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (canthophyll de-epoxidation state) minus the early morning PRI (canthophyll peoxidation state) yielded the corrected PRI (APRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (\(\text{SIF}_{\text{TOTC}\_{760}}\)) was downscaled to the broadband photosystem-level SIF for photosystem II (\(\text{SIF}_{\text{TOTC}\_{\text{FULL}\_{\text{PSII}}}}\)). Our results show that GPP can be accurately estimated using a multi-linear model with \(\text{SIF}_{\text{TOTC}\_{\text{FULL}\_{\text{PSII}}}}\) and \(\Delta\text{PRI}\). The ratio of GPP measured using the eddy covariance (EC) method (\(\text{GPP}_{\text{EC}}\)) to GPP estimated using \(\text{SIF}_{\text{TOTC}\_{\text{FULL}\_{\text{PSII}}}}\) and \(\Delta\text{PRI}\) exhibited a non-linear correlation with the CI along both the half-hourly (\(\text{R}^{2}=0.21\)) and daily scales (\(\text{R}^{2}=0.25\)). The GPP estimates using \(\text{SIF}_{\text{TOTC}\_{\text{FULL}\_{\text{PSII}}}}\) and \(\Delta\text{PRI}\) were significantly improved by the addition of the CI (for the half-hourly data, \(\text{R}^{2}\) improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 \(\text{\SIUnitSymbolMicro m}\text{ol}\text{ol}\text{ol}\text{ol}\text{ol}\text{ol}  
efficiency (LUE) models (e.g., MODIS-LUE, VPM, and EC-LUE) have been suggested [7; 8; 9; 10; 11]. The flux measured using the EC method can only represent ground GPP in a limited spatial region. Due to the fact that vegetation indices (VIs) are generally representative of vegetation greenness, Vis-based statistical models only track some dynamics of photosynthesis and clearly lack consideration of the influence of climate conditions on the growth of plants. Compared to the Vis-based statistical models, the LUE model has better mechanisms, fewer parameters, and better spatiotemporal scalability, making it widely used at the regional scale. However, the main parameters of these models are generally based on empirical relationships, which pose many uncertainties. In addition, this model focuses on the vegetation-soil-atmosphere continuum as the research object, with better mechanistic properties, but there are many complex parameters, which leads to relatively few applications at a large scale [9]. Therefore, a predicted model with photosynthetic process information and simple parameters is important for tracking the dynamic variations in GPP at large scales, especially under different environmental conditions. It has been established that the energy dissipation pathway known as solar-induced chlorophyll fluorescence (SIF), which is directly related to light reactions, is the superior way to monitor GPP dynamics, particularly under stressful conditions [5; 12; 13]. SIF and GPP have an inherent theoretical foundation for coupling since absorbed photosynthetically active radiation (APAR) drives both SIF and GPP. In reality, the fundamental mechanism for SIF-based GPP estimation is intimately linked to the linear electron transfer rate (ETR), which is coupled with carboxylation processes [11; 14]. Many studies have demonstrated that SIF is closely related to GPP and can be used to track the dynamic changes in GPP in response to stress conditions, even for evergreen forests with an almost invariant canopy structure [12].