More Like this

1. Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence

   https://doi.org/10.1088/1748-9326/ac9dae  

   Cheng, Rui; Magney, Troy S; Orcutt, Erica L; Pierrat, Zoe; Köhler, Philipp; Bowling, David R; Bret-Harte, M Syndonia; Euskirchen, Eugénie S; Jung, Martin; Kobayashi, Hideki; et al (November 2022, Environmental Research Letters)

   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. 

   more » « less
2. Recent Advances and Challenges in Monitoring and Modeling Non-Growing Season Carbon Dioxide Fluxes from the Arctic Boreal Zone

   https://doi.org/10.1007/s40641-023-00190-4  

   Arndt, Kyle A.; Hashemi, Josh; Natali, Susan M.; Schiferl, Luke D.; Virkkala, Anna-Maria (October 2023, Current Climate Change Reports)

   Abstract Purpose of ReviewWhile previously thought to be negligible, carbon emissions during the non-growing season (NGS) can be a substantial part of the annual carbon budget in the Arctic boreal zone (ABZ), which can shift the carbon balance of these ecosystems from a long-held annual carbon sink towards a net annual carbon source. The purpose of this review is to summarize NGS carbon dioxide (CO₂) flux research in the ABZ that has been published within the past 5 years. Recent FindingsWe explore the processes and magnitudes of CO₂fluxes, and the status of modeling efforts, and evaluate future directions. With technological advances, direct measurements of NGS fluxes are increasing at sites across the ABZ over the past decade, showing ecosystems in the ABZ are a large source of CO₂in the shoulder seasons, with low, consistent, winter emissions. SummaryEcosystem carbon cycling models are being improved with some challenges, such as modeling below ground and snow processes, which are critical to understanding NGS CO₂fluxes. A lack of representative in situ carbon flux data and gridded environmental data are leading limiting factors preventing more accurate predictions of NGS carbon fluxes. 

   more » « less
3. Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget

   https://doi.org/10.1111/gcb.16553  

   Watts, Jennifer D.; Farina, Mary; Kimball, John S.; Schiferl, Luke D.; Liu, Zhihua; Arndt, Kyle A.; Zona, Donatella; Ballantyne, Ashley; Euskirchen, Eugénie S.; Parmentier, Frans‐Jan W.; et al (January 2023, Global Change Biology)

   Abstract Arctic‐boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic‐boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003–2015) vegetation gross primary productivity (GPP), ecosystem respiration (R_eco), net ecosystem CO₂exchange (NEE;R_eco − GPP), and terrestrial methane (CH₄) emissions for the Arctic‐boreal zone using a satellite data‐driven process‐model for northern ecosystems (TCFM‐Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM‐Arctic to obtain daily 1‐km²flux estimates and annual carbon budgets for the pan‐Arctic‐boreal region. Across the domain, the model indicated an overall average NEE sink of −850 Tg CO₂‐C year⁻¹. Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH₄emissions from tundra and boreal wetlands (not accounting for aquatic CH₄) were estimated at 35 Tg CH₄‐C year⁻¹. Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high‐latitude carbon status and also indicates a continued need for integrated site‐to‐regional assessments to monitor the vulnerability of these ecosystems to climate change. 

   more » « less
4. A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks

   https://doi.org/10.1186/s40658-023-00558-3  

   Rahman, Md_Ashequr; Li, Zekun; Yu, Zitong; Laforest, Richard; Thorek, Daniel_L_J; Jha, Abhinav_K (June 2023, EJNMMI Physics)

   Abstract BackgroundSingle-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for$$\alpha$$ $\alpha$-particle radiopharmaceutical therapies ($$\alpha$$ $\alpha$-RPTs). However, quantitative SPECT for$$\alpha$$ $\alpha$-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks. MethodsGiven the low-count setting, it is important that the reconstruction method extracts the maximal possible information from each detected photon. Processing data over multiple energy windows and in list-mode (LM) format provide mechanisms to achieve that objective. Towards this goal, we propose a list-mode multi energy window (LM-MEW) ordered-subsets expectation–maximization-based SPECT reconstruction method that uses data from multiple energy windows in LM format and include the energy attribute of each detected photon. For computational efficiency, we developed a multi-GPU-based implementation of this method. The method was evaluated using 2-D SPECT simulation studies in a single-scatter setting conducted in the context of imaging [$$^{223}$$ ${}^{223}$Ra]RaCl$${_2}$$ ${}_2$, an FDA-approved RPT for metastatic prostate cancer. ResultsThe proposed method yielded improved performance on the task of estimating activity uptake within known regions of interest in comparison to approaches that use a single energy window or use binned data. The improved performance was observed in terms of both accuracy and precision and for different sizes of the region of interest. ConclusionsResults of our studies show that the use of multiple energy windows and processing data in LM format with the proposed LM-MEW method led to improved quantification performance in low-count SPECT of isotopes with multiple emission peaks. These results motivate further development and validation of the LM-MEW method for such imaging applications, including for$$\alpha$$ $\alpha$-RPT SPECT. 

   more » « less
5. Forests for forests: combining vegetation indices with solar-induced chlorophyll fluorescence in random forest models improves gross primary productivity prediction in the boreal forest

   https://doi.org/10.1088/1748-9326/aca5a0  

   Pierrat, Zoe Amie; Bortnik, Jacob; Johnson, Bruce; Barr, Alan; Magney, Troy; Bowling, David R; Parazoo, Nicholas; Frankenberg, Christian; Seibt, Ulli; Stutz, Jochen (December 2022, Environmental Research Letters)

   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. 

   more » « less