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.

The Arctic-Boreal Zone (ABZ) is characterized by spatially heterogeneous vegetation composition and structure, leading to challenges for inferring patterns in vegetation productivity. A mechanistic understanding of the patterns and processes underlying spectral remote sensing observations is necessary to overcome these challenges. Solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and chlorophyll/carotenoid index (CCI) show promise for tracking productivity and disentangling links to the activity and distribution of chlorophyll at coarse spatial scales (e.g. 0.5°), but their effectiveness for studying mixed landscapes characteristic of the ABZ remains unclear. Here, we use airborne observations collected during NASA’s Arctic-Boreal Vulnerability Experiment to examine the spatial covariation between SIF, NIRv, and CCI at a scale (30 m) commensurate with the best available landcover products across interior Alaska. Additionally, we compare relationships among SIF and vegetation indices from spaceborne observations (TROPOMI and MODIS) resampled to a 0.01° (∼1000 m) scale. We find that the strength of the SIF-NIRv linear relationship degrades when compared from the spaceborne to the airborne scale (R 2 = 0.50 vs. 0.26) as does the strength of the SIF-CCI linear relationship (R 2 = 0.30 vs. 0.18), though the degradation of SIF-CCI is less severe than that of SIF-NIRv. The relationship of SIF with either vegetation index is strongly dependent on landcover class at both airborne and spaceborne scales. We provide context for how further work could leverage SIF with reflectance indices measurable from a variety of platforms to improve mapping of vegetation dynamics in this ecoregion.

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.

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