The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information. Plain Language SummaryThe boreal forest is one of the most sensitive regions on the planet to climate change, yet its sensitivity remains poorly understood. In particular, the timing and drivers of the spring transition, as the forest changes from a winter adapted state to a summer adapted state, carry significant uncertainties. Remote sensing metrics can be used to characterize the spring transition, but their interpretation is complicated by persistent greenness, frequent snow cover, and a high prevalence of forests containing both deciduous and evergreen species. We collected tower-based remotely sensed metrics, stem radius, and carbon uptake measurements and show that the spring transition occurs in two distinct phases. The first phase is a reactivation of photosynthesis in evergreens and is triggered by thawed stems, warm air temperature, and moist soil. The second phase is a change in evergreen photoprotective pigment levels and the leaf-out of deciduous species. It is triggered by soil thaw. Both phases were detected with different remote sensing metrics that depended on species type. Our results illustrate how satellite measurements could be improved to capture the spring transition over diverse landscapes and what environmental factors control the spring transition.

Uncertainties in future climate projections are, in large part, due to an incomplete understanding of terrestrial carbon and ecosystem feedbacks (Friedlingstein et al., 2014). Among the most poorly understood ecosystems is the boreal forest, which stores a significant amount of carbon and is one of the regions most sensitive to environmental change (

Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including NASA's Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low-stature plants and very fine-scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long-term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi-scale spectroscopy, from lab studies to satellites that enable frequent and continuous long-term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

The reflectance of vegetation (Gates et al., 1965;Shull, 1929), and its scaling from leaf to canopy, reveals information on vegetation health, taxonomic and functional composition, and ecosystem processes. Early vegetation indices (Kriegler et al., 1969) linked light reflectance in the red and near-infrared to chlorophyll and leaf water content (Tucker, 1979). Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have since been used to infer net primary production (NPP) and biomass accumulation (Tucker & Sellers, 1986). These indices have advanced to more precisely control for the effects of atmospheric conditions, that is, the

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 1) remotely sensed products contain information relevant for understanding GPP dynamics, 2) random forest models capture quantitative SIF, GPP, and light availability relationships, and 3) 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.

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: 1) 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 2) 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: 1) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; 2) changing biomass distribution and SIF-GPP relationship along elevational gradients, and 3) 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.

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Observing the environment in the vast inaccessible regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including