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1. 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. 

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2. From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

   https://doi.org/10.1111/gcb.16646  

   Sun, Ying; Wen, Jiaming; Gu, Lianhong; Joiner, Joanna; Chang, Christine Y.; van der Tol, Christiaan; Porcar‐Castell, Albert; Magney, Troy; Wang, Lixin; Hu, Leiqiu; et al (June 2023, Global Change Biology)

   Abstract Although our observing capabilities of solar‐induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in‐situ SIF observing capability especially in “data desert” regions, improving cross‐instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data. 

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3. Synergies among environmental science research and monitoringnetworks: A research agenda

   https://doi.org/10.1029/2020EF001631  

   Jones, J. A.; Groffman, P. M.; Blair, J.; Davis, F. W.; Dugan, H.; Euskirchen, E. E.; Frey, S. D.; Harms, T. K.; Hinckley, E.; Kosmala, M.; et al (December 2020, Earth's Future)

   null (Ed.)

   Many research and monitoring networks in recent decades have provided publicly available data documenting environmental and ecological change, but little is known about the status of efforts to synthesize this information across networks. We convened a working group to assess ongoing and potential cross‐network synthesis research and outline opportunities and challenges for the future, focusing on the US‐based research network (the US Long‐Term Ecological Research network, LTER) and monitoring network (the National Ecological Observatory Network, NEON). LTER‐NEON cross‐network research synergies arise from the potentials for LTER measurements, experiments, models, and observational studies to provide context and mechanisms for interpreting NEON data, and for NEON measurements to provide standardization and broad scale coverage that complement LTER studies. Initial cross‐network syntheses at co‐located sites in the LTER and NEON networks are addressing six broad topics: how long‐term vegetation change influences C fluxes; how detailed remotely‐sensed data reveal vegetation structure and function; aquatic‐terrestrial connections of nutrient cycling; ecosystem response to soil biogeochemistry and microbial processes; population and species responses to environmental change; and disturbance, stability and resilience. This initial work offers exciting potentials for expanded cross‐network syntheses involving multiple long‐term ecosystem processes at regional or continental scales. These potential syntheses could provide a pathway for the broader scientific community, beyond LTER and NEON, to engage in cross‐network science. These examples also apply to many other research and monitoring networks in the US and globally, and can guide scientists and research administrators in promoting broad‐scale research that supports resource management and environmental policy. 

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4. Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California’s Sierra Nevada mountains

   https://doi.org/10.1088/1748-9326/ad07b4  

   Kunik, Lewis; Bowling, David R; Raczka, Brett; Frankenberg, Christian; Köhler, Philipp; Cheng, Rui; Smith, Kenneth R; Goulden, Michael; Jung, Martin; Lin, John C (November 2023, Environmental Research Letters)

   Abstract 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. 

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5. Solar-Induced Chlorophyll Fluorescence (SIF): Towards a Better Understanding of Vegetation Dynamics and Carbon Uptake in Arctic-Boreal Ecosystems

   https://doi.org/10.1007/s40641-024-00194-8  

   Cheng, Rui (June 2024, Current Climate Change Reports)

   Abstract Purpose of ReviewTerrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO$$^{2}$$ ${}^2$uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes. Recent FindingsCross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming. SummaryWhile SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections. 

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