Find research datasets worth reusing

opencc-by-4.0Jan 2020View details →

Data supporting "Leaf carbon and water status control stomatal and non-stomatal limitations of photosynthesis in trees."

Data supporting our New Phytologist publication: "Leaf carbon and water status control stomatal and non-stomatal limitations of photosynthesis in trees"

opencc-by-4.0Dec 2018View details →

Growth temperature influence on lipids and photosynthesis in Lepidium sativum.

Dataset relative to the paper: Growth temperature influence on lipids and photosynthesis in Lepidium sativum. Including: Phenotypical analysis (Leaf area and hypocotyl lenght) phen.xlsx Weight and water content of the samples (Weight.xlsx) Chlorophyll content and a/b ratio (chl_R.xlsx) Maximum yield of Photosystem II (QY_MAX.xlsx) Thermal dissipation at different light intensity npq.xlsx Electron transport, fraction of open PSII reaction centers (qL) and quantum efficiency (qY) at different light intensity qL.xlsx Prenyl-lipid relative quantification and galactolipid profiling and analysis (Lipidomic_Analysis_2020.xls) Analysis of ECL signal of the immunodetection of proteins from the main photosynthetic complexes WB_R.2.xlsx Analysis of the kinetics of Vj after exposure to increasing time in the dark (Vj_dark.xlsx) or far red light (Vj_FAR.xlsx) R script used for data analysis and visualization. ( R_Script.R )

opencc-zeroDec 2019View details →

Data from: Can light-saturated photosynthesis in lowland tropical forests be estimated by one light level?

Leaf-level net photosynthesis (An) estimates and associated photosynthetic parameters are crucial for accurately parameterizing photosynthesis models. For tropical forests such data are poorly available and collected at variable light conditions. To avoid over- or underestimation of modelled photosynthesis, it is critical to know at which photosynthetic photon flux density (PPFD) photosynthesis becomes light saturated. We studied the dependence of An on PPFD in two tropical forests in French Guiana. We estimated the light saturation range, including the lowest PPFD level at which Asat (An at light saturation) is reached, as well as the PPFD range at which Asat remained unaltered. The light saturation range was derived from photosynthetic light-response curves, and within-canopy and interspecific differences were studied. We observed wide light saturation ranges of An. Light saturation ranges differed among canopy heights, but a PPFD level of 1000 µmol/m²/s was common across all heights, except for pioneer trees species that did not reach light saturation below 2000 µmol/m²/s. A light intensity of 1000 µmol/m²/s sufficed for measuring Asat of climax species at our study sites, independent of the species or the canopy height. Because of the wide light saturation ranges, results from studies measuring Asat at higher PPFD levels (for upper canopy leaves up to 1600 µmol/m²/s) are comparable with studies measuring at 1000 µmol/m²/s.

opencc-zeroJul 2020View details →

Data from: Temperature amplifies the effect of high CO2 on the photosynthesis, respiration and calcification of the coralline algae Phymatolithon lusitanicum

The combination of ocean acidification (OA) and global warming is expected to have a significant effect on the diversity and functioning of marine ecosystems, particularly on calcifying algae such as rhodoliths (maërl) that form extensive beds worldwide, from polar to tropical regions. In addition, the increasing frequency of extreme events, such as heatwaves, threaten coastal ecosystems and may affect their capacity to fix blue carbon. The few studies where the simultaneous effects of both temperature and CO2 were investigated have revealed contradictory results. To assess the effect that high temperature spells can have on the maërl beds under OA, we tested the short-time effects of temperature and CO2 on the net photosynthesis, respiration and calcification of the recently described species Phymatolithon lusitanicum, the most common maërl species of southern Portugal. Photosynthesis, calcification and respiration increased with temperature, and the differences among treatments were enhanced under high CO2. We found that in the short term, the metabolic rates of Phymatolithon lusitanicum will increase with CO2 and temperature as will the coupling between calcification and photosynthesis. However, under high CO2, this coupling will favor photosynthesis over calcification, which, in the long term, can have a negative effect on the blue carbon fixing capacity of the maërl beds from southern Portugal.

opencc-zeroApr 2020View details →

C4 photosynthesis and the economic spectra of leaf and root traits independently influence growth rates in grasses

Photosynthetic pathway is an important cause of growth rate variation between species, such that the enhanced carbon uptake of C4 species leads to faster growth than their C3 counterparts. Leaf traits that promote rapid resource acquisition may further enhance the growth capacity of C4 species. However, how root economic traits interact with leaf traits, and the different growth strategies adopted by plants with C3 and C4 photosynthetic pathways is unclear. Plant economic traits could interact with, or act independently of, photosynthetic pathway in influencing growth rate, or C3 and C4 species could segregate out along a common growth rate-trait relationship. We measured leaf and root traits on 100+ grass species grown from seeds in a controlled, common environment to compare with relative growth rates (RGR) during the initial phase of rapid growth, controlling for phylogeny and allometric effects. Photosynthetic pathway acts independently to leaf and root functional traits in causing fast growth. Using C4 photosynthesis, plants can achieve faster growth than their C3 counterparts (by an average 0.04 g g-1 day-1) for a given suite of functional trait values, with lower investments of leaf and root nitrogen. Leaf and root traits had an additive effect on RGR, with plants achieving fast growth by possessing resource-acquisitive leaf traits (high specific leaf area and low leaf dry matter content) or root traits (high specific root length and area, and low root diameter), but having both leads to an even faster growth rate (by up to 0.06 g g-1 day-1). C4 photosynthesis can provide a greater relative increase in RGR for plants with a 'slow' ecological strategy than in those with fast growth. However, aboveground and belowground strategies are not coordinated, so that species can have any combination of 'slow' or 'fast' leaf and root traits. Synthesis: C4 photosynthesis increases growth rate for a given combination of economic traits, and significantly alters plant nitrogen economy in the leaves and roots. However, leaf and root economic traits act independently to further enhance growth. The fast growth of C4 grasses promotes a competitive advantage under hot, sunny conditions.

opencc-zeroDec 2018View details →

Data from: Plastome phylogenetics of tribe Eriachneae and evolution of C₄ photosynthesis in subfamily Micrairoideae (Poaceae)

Tribe Eriachneae in subfamily Micrairoideae is one of the least explored of the ca. 22 C4 lineages in the grass family (Poaceae). Whereas many C4 lineages are more species-rich, more morphologically disparate, and wider ranging than their C3 sisters, Eriachneae has fewer species, less disparity, and covers a far smaller geographical area than its C3 sister tribe Isachneae. Tribe Micraireae, which is C3 and sister to Eriachneae and Isachneae, occupies habitats more similar to C4 Eriachneae than C3 Isachneae. Evolutionary analyses within the subfamily are hindered by the lack of a phylogenetic framework for any substantial sample of species. Additionally, only a handful of members of Micrairoideae have been tested for photosynthetic pathway. This study presents the first well-resolved phylogeny of Eriachneae based on full plastome sequences from almost half of the species in the tribe. Photosynthetic pathway is tested for 47 species representing all three tribes of Micrairoideae using carbon isotopes to test the assumption that C4 is restricted to Eriachneae. Habitat preferences among the tribes are estimated using bioclimatic data, with multivariate analyses showing that habitats of Micraireae and Eriachneae are more similar to each other than either is to Isachneae, and the range of environments is greater in Isachneae than in Eriachneae. All measured Eriachneae are confirmed to be C4, and all Isachneae are C3. Evolutionary interpretation of these results is necessarily preliminary, and greater phylogenetic sampling in Isachneae is needed to estimate diversification rates and ancestral habitats, but subfamily Micrairoideae appears to be an interesting exception to general patterns of C4 evolution in grasses.

opencc-zeroDec 2016View details →

Data from: Targeted enrichment of large gene families for phylogenetic inference: phylogeny and molecular evolution of photosynthesis genes in the Portullugo clade (Caryophyllales)

Hybrid enrichment is an increasingly popular approach for obtaining hundreds of loci for phylogenetic analysis across many taxa quickly and cheaply. The genes targeted for sequencing are typically single-copy loci, which facilitate a more straightforward sequence assembly and homology assignment process. However, this approach limits the inclusion of most genes of functional interest, which often belong to multi-gene families. Here we demonstrate the feasibility of including large gene families in hybrid enrichment protocols for phylogeny reconstruction and subsequent analyses of molecular evolution, using a new set of bait sequences designed for the "portullugo" (Caryophyllales), a moderately sized lineage of flowering plants (∼2200 species) that includes the cacti and harbors many evolutionary transitions to C4 and CAM photosynthesis. Including multi-gene families allowed us to simultaneously infer a robust phylogeny and construct a dense sampling of sequences for a major enzyme of C4 and CAM photosynthesis, which revealed the accumulation of adaptive amino acid substitutions associated with C4 and CAM origins in particular paralogs. Our final set of matrices for phylogenetic analyses included 75–218 loci across 74 taxa, with ∼50% matrix completeness across datasets. Phylogenetic resolution was greatly improved across the tree, at both shallow and deep levels. Concatenation and coalescent-based approaches both resolve the sister lineage of the cacti with strong support: Anacampserotaceae + Portulacaceae, two lineages of mostly diminutive succulent herbs of warm, arid regions. In spite of this congruence, BUCKy concordance analyses demonstrated strong and conflicting signals across gene trees. Our results add to the growing number of examples illustrating the complexity of phylogenetic signals in genomic-scale data.

opencc-by-4.0Dec 2023View details →

Data for Wildfire-induced Increases in Photosynthesis in Boreal Forest Ecosystems of North America

<div> Jupyter notebooks and datasets for Kim et al. (2024), Global Change Biology Kim, J. E., Wang, J. A., Li, Y., Czimczik, C. I., & Randerson, J. T. (2024). Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America. Global Change Biology, 30, e17151. <a href="https://doi.org/10.1111/gcb.17151">https://doi.org/10.1111/gcb.17151</a> </div>

UFLUX 100m daily photosynthesis fluxes in southwestern UK 2020

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2020</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in southeastern UK 2020

<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2020</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul> </div>

UFLUX 100m daily photosynthesis fluxes in in southwestern UK 2021

<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2021</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul> </div>

UFLUX 100m daily photosynthesis fluxes in northwestern UK 2021

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2021</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in southwestern UK 2022

<div> <h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southwestern UK 2022</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul> </div>

UFLUX 100m daily photosynthesis fluxes in southeastern UK 2021

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2021</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in northwestern UK 2022

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2022</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in northwestern UK 2020

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northwestern UK 2020</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in southeastern UK 2022

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2022</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in northeastern UK 2020

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northeastern UK 2020</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>

UFLUX 100m daily photosynthesis fluxes in northeastern UK 2021

<h3>UFLUX Ensemble UK100mdaily (UK 100 Daily) in northeastern UK 2021</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components: <ul> <li> Gross Primary Production (GPP) [open access] </li> <li> Ecosystem Respiration (RECO) [in progress] </li> <li> Net Ecosystem Exchange (NEE) [in progress] </li> <li> Sensible Heat Flux (H) [in progress] </li> <li> Latent Energy Flux (LE) [in progress] </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul>