Abstract Arctic wetlands are known methane (CH 4 ) emitters but recent studies suggest that the Arctic CH 4 sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH 4 using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH 4 occurred at all sites at rates of 0.092 ± 0.011 mgCH 4 m −2 h −1 (mean ± s.e.), CH 4 uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH 4 uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH 4 uptake by Arctic soils, providing a negative feedback to global climate change.

• A new PRI based LUE estimation method was proposed. • This method separated the half hourly observed PRI into PRI0 and ΔPRI. • PRI0 indicated daily maximal light use efficiency (LUE max ). • The ΔPRI linked PRI to different diurnal meteorological stress conditions. • This new method significantly improves LUE accuracy (R 2 from 0.1 to 0.7). Photosynthetic light use efficiency (LUE) determines the ability of a plant to assimilate atmospheric carbon dioxide to biomass and is known to be controlled by environmental conditions, light regimes and forest age. The photochemical reflectance index (PRI), derived from leaf or canopy remotely sensed spectra, has been shown to be an effective and accurate estimator of LUE. In this study, we propose a new LUE estimation method that separates the PRI into daily maximal PRI (PRI0) for indicating daily maximal light use efficiency (LUE max ) and ΔPRI, defined as the difference between PRI0 and instantaneous PRI, for estimating the diurnal physiological stress ( fstress) . We develop and apply the method across three temperate pine stands and a deciduous stand of different ages, in Southern Ontario, Canada. Half hourly canopy level spectra were acquired from a tower-based spectro-radiometer system (AMSPEC-III) over the growing season at the four stands. Results show that the PRI0 predicted well LUE max (R 2 > 0.6, p < 0.05) in both coniferous and deciduous stands and was able to track seasonal changes in pigment pools sizes. The ΔPRI was sensitive to short-term meteorological conditions, specifically temperature, vapor pressure deficit (VPD), and light variations resulting in strong correlations ( p < 0.05 ) with fstress and half hourly LUE. This new method significantly improves the estimation accuracy (R 2 increases from 0.1 to around 0.7) for PRI-based LUE estimation across all four stands of varying age and species composition and suggests that PRI-based LUE estimation has the ability to inform on both the effects of seasonal and diurnal change in photosynthetic efficiency under different meteorological conditions.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.