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Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.

 

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 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 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. 

Abstract. The flow of carbon through terrestrial ecosystems and the response toclimate are critical but highly uncertain processes in the global carboncycle. However, with a rapidly expanding array of in situ and satellitedata, there is an opportunity to improve our mechanistic understanding ofthe carbon (C) cycle's response to land use and climate change. Uncertaintyin temperature limitation on productivity poses a significant challenge topredicting the response of ecosystem carbon fluxes to a changing climate.Here we diagnose and quantitatively resolve environmental limitations onthe growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpineevergreen forest in Colorado, USA. We implement the CARbonDAta-MOdel fraMework (CARDAMOM) model–datafusion network to resolve the temperature sensitivity of spring GPP. Tocapture a GPP temperature limitation – a critical component of the integratedsensitivity of GPP to temperature – we introduced a cold-temperature scalingfunction in CARDAMOM to regulate photosynthetic productivity. We found thatGPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C(±1.1 ∘C). The addition of this scaling factor improvedthe model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of the dataassimilation training period (r=0.88). While cold-temperaturelimitation has an important influence on spring GPP, it does not have asignificant impact on integrated growing-season GPP, revealing that otherenvironmental controls, such as precipitation, play a more important role inannual productivity. This study highlights growing-season onset temperatureas a key limiting factor for spring growth in winter-dormant evergreenforests, which is critical in understanding future responses to climatechange.