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Abstract Pigment dynamics in temperate evergreen forests remain poorly characterized, despite their year-round photosynthetic activity and importance for carbon cycling. Developing rapid, nondestructive methods to estimate pigment composition enables high-throughput assessment of plant acclimation states. In this study, we investigate the seasonality of eight chlorophyll and carotenoid pigments and hyperspectral reflectance data collected at both the needle (400–2400 nm) and canopy (420–850 nm) scales in Pinus palustris (longleaf pine) at the Ordway Swisher Biological Station in north-central Florida, USA. Needle spectra were obtained at three distinct times throughout the year, while tower-based spectra were collected continuously over a nine-month period. Seasonal trends in photoprotective pigments (e.g., lutein and xanthophylls) and photosynthetic pigments (e.g., chlorophylls) aligned closely with seasonal changes in photosynthetically active radiation and gross primary productivity. To track inter-tree and seasonal variability in pigment pools with hyperspectral reflectance data, we used correlation analyses and ridge regression models. Ridge regression models using the full hyperspectral range outperformed predictions using standard linear regression with specific wavelengths in a normalized difference index fashion. Ridge regression successfully predicted all pigment pools (R2 > 0.5) with comparable accuracy at both the needle and canopy scales. The models performed best for lutein, neoxanthin, antheraxanthin, and chlorophyll a and b - which had greater inter-tree and seasonal variation - and achieved moderate accuracy for violaxanthin, alpha-carotene, and beta-carotene. These results provide a foundation for scaling biochemical traits from ground-based sensors to airborne and satellite platforms, particularly in ecosystems with subtle changes in pigment dynamics. 

Modeling of atmosphere–snow exchange provides insight into fundamental processes driving pollutant deposition. Gas properties, such as solubility and stickiness to ice, influence the role of the snowpack as a trace gas reservoir and chemical reactor. 

The Alaskan Layered Pollution And Chemical Analysis (ALPACA)-2022 field study (https://alpaca.community.uaf.edu/alpaca-field-study/) investigated air pollution under dark and cold conditions in Fairbanks, Alaska in January and February, 2022. One of the main motivations for ALPACA was to understand how temperature inversions trap pollutants at the surface. This was studied by using University of California, Los Angeles (UCLA) long-path Differential Optical Absorption Spectroscopy instrument (LP-DOAS) to probe the Fairbanks atmosphere from 12 meters(m) – 191m altitude and yield information on the vertical distribution of various trace gases / pollutants. The dataset contains the open atmosphere LP-DOAS measurement of Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Formaldehyde (HCHO), and Nitrous Acid (HONO) on four different light paths over wintertime downtown Fairbanks, Alaska (AK) during ALPACA. The four light paths cover the following altitude intervals: 12-17m, 17-73m, 17-115m, and 17-191m. The ReadMe file included in the data set provides exact coordinates, length, and altitude intervals for the four light paths. 

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. Subarctic cities notoriously experience severe winter pollution episodes with fine particle (PM2.5) concentrations above 35 µg m−3, the US Environmental Protection Agency (EPA) 24 h standard. While winter sources of primary particles in Fairbanks, Alaska, have been studied, the chemistry driving secondary particle formation is elusive. Biomass burning is a major source of wintertime primary particles, making the PM2.5 rich in light-absorbing brown carbon (BrC). When BrC absorbs sunlight, it produces photooxidants – reactive species potentially important for secondary sulfate and secondary organic aerosol formation – yet photooxidant measurements in high-latitude PM2.5 remain scarce. During the winter of 2022 Alaskan Layered Pollution And Chemical Analysis (ALPACA) field campaign in Fairbanks, we collected PM filters, extracted the filters into water, and exposed the extracts to simulated sunlight to characterize the production of three photooxidants: oxidizing triplet excited states of BrC, singlet molecular oxygen, and hydroxyl radical. Next, we used our measurements to model photooxidant production in highly concentrated aerosol liquid water. While conventional wisdom indicates photochemistry is limited during high-latitude winters, we find that BrC photochemistry is significant: we predict high triplet and singlet oxygen daytime particle concentrations up to 2×10-12 and 3×10-11 M, respectively, with moderate hydroxyl radical concentrations up to 5×10-15 M. Although our modeling predicts that triplets account for 0.4 %–10 % of daytime secondary sulfate formation, particle photochemistry cumulatively dominates, generating 76 % of daytime secondary sulfate formation, largely due to in-particle hydrogen peroxide, which contributes 25 %–54 %. Finally, we estimate triplet production rates year-round, revealing the highest rates in late winter when Fairbanks experiences severe pollution and in summer when wildfires generate BrC. 

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. 

Abstract Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but the biological and physical controls on ENF carbon cycle feedback loops are poorly understood and difficult to measure. To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques designed to detect ENF photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biological and biophysical processes control the fate of photons from leaf to globe, ultimately enabling remote estimates of ENF photosynthesis. We demonstrate this using data across four ENF sites spanning a broad range of environmental conditions and link leaf- and stand-scale observations of photosynthesis (i.e., needle biochemistry and flux towers) with tower- and satellite-based remote sensing. The multidisciplinary nature of this work can serve as a model for the coordination and integration of observations made at multiple scales. 

Abstract Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region.