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Abstract The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIF_relative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r² = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUE_Pand SIF_relativewhich were well correlated across all sites (r² = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. 

Abstract Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes.