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Abstract Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Abstract Aquatic networks contribute greenhouse gases and lateral carbon (C) export from catchments. The magnitudes of these fluxes exceed the global land C sink but are uncertain. Resolving this uncertainty is important for understanding climate feedbacks. We quantified vertical methane (CH4) and carbon dioxide (CO2) emissions from lakes and streams, and lateral export of dissolved inorganic and organic carbon from a hemiboreal catchment for 3 yr. Lateral C fluxes dominated the total aquatic C flux. All aquatic C fluxes were disproportionately contributed from spatially restricted areas and/or short‐term events. Hence, consideration of local and episodic variability is vital. Temperature and runoff were the main temporal drivers for lake and stream C emissions, respectively. Whole‐catchment aquatic C emissions scaled linearly with these drivers within timeframes of stable land‐cover. Hence, temperature and runoff increase across Northern Hemisphere humid areas from climate change may yield proportional increases in aquatic C fluxes.

Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE = 9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.

Nitrogen (N) fertilization can increase stem wood production by several hundred percent in boreal forests. At the same time, there are concerns about the environmental consequences of N fertilization, especially considering losses of the greenhouse gas nitrous oxide (N2O) to the atmosphere. Soils are a large contributor to N2O emissions on a global scale. The aim of this study was to investigate the consequences of long-term nutrient optimization fertilization on N2O emissions in a boreal forest in Northern Sweden. Field N2O flux measurements were conducted during 2 years with manual and automatic chambers, as well as gas probes in the snow. The N2O emissions were generally low during the whole period of measurements, both from the control and fertilized plots. The emissions were generally highest during the winters, as well as the variability in the observed values. Overall, N2O emissions from fertilized plots were about twice the control, which could be explained by changes in the soil carbon-to-nitrogen ratio.

In a global field survey across a wide range of organic soils, the authors find that N2O flux can be predicted by models incorporating soil nitrate concentration (NO3 –), water content and temperature. N2O emission increases with NO3 – and temperature and follows a bell-shaped distribution with water content.

In Europe, willow (Salix spp.) trees have been used commercially since the 1980s at a large scale to produce renewable energy. While reducing fossil fuel needs, growing short rotation coppices (SRCs), such as poplar or willow, may have a high impact on local air quality as these species are known to produce high amounts of isoprene, which can lead to the production of tropospheric ozone (O3). Here, we present a long-term leaf-scale study of biogenic volatile organic compound (BVOC) emissions from a Swedish managed willow site with the aim of providing information on the seasonal variability in BVOC emissions during two growing seasons, 2015–2016. Total BVOC emissions during these two seasons were dominated by isoprene (>96% by mass) and the monoterpene (MT) ocimene. The average standardized (STD, temperature of 30 °C and photosynthetically active radiation of 1000 µmol m−2 s−1) emission rate for isoprene was 45.2 (±42.9, standard deviation (SD)) μg gdw−1 h−1. Isoprene varied through the season, mainly depending on the prevailing temperature and light, where the measured emissions peaked in July 2015 and August 2016. The average STD emission for MTs was 0.301 (±0.201) μg gdw−1 h−1 and the MT emissions decreased from spring to autumn. The average STD emission for sesquiterpenes (SQTs) was 0.103 (±0.249) μg gdw−1 h−1, where caryophyllene was the most abundant SQT. The measured emissions of SQTs peaked in August both in 2015 and 2016. Non-terpenoid compounds were grouped as other VOCs (0.751 ± 0.159 μg gdw−1 h−1), containing alkanes, aldehydes, ketones, and other compounds. Emissions from all the BVOC groups decreased towards the end of the growing season. The more sun-adapted leaves in the upper part of the plantation canopy emitted higher rates of isoprene, MTs, and SQTs compared with more shade-adapted leaves in the lower canopy. On the other hand, emissions of other VOCs were lower from the upper part of the canopy compared with the lower part. Light response curves showed that ocimene and α-farnesene increased with light but only for the sun-adapted leaves, since the shade-adapted leaves did not emit ocimene and α-farnesene. An infestation with Melampsora spp. likely induced high emissions of, e.g., hexanal and nonanal in August 2015. The results from this study imply that upscaling BVOC emissions with model approaches should account for seasonality and also include the canopy position of leaves as a parameter to allow for better estimates for the regional and global budgets of ecosystem emissions.

Across a landscape, aquatic-terrestrial interfaces within and between ecosystems are hotspots of organic matter (OM) mineralization. These interfaces are characterized by sharp spatio-temporal changes in environmental conditions, which affect OM properties and thus control OM mineralization and other transformation processes. Consequently, the extent of OM movement at and across aquatic-terrestrial interfaces is crucial in determining OM turnover and carbon (C) cycling at the landscape scale. Here, we propose expanding current concepts in aquatic and terrestrial ecosystem sciences to comprehensively evaluate OM turnover at the landscape scale. We focus on three main concepts toward explaining OM turnover at the landscape scale: the landscape spatio-temporal context, OM turnover described by priming and ecological stoichiometry, and anthropogenic effects as a disruptor of natural OM transfer magnitudes and pathways. A conceptual framework is introduced that allows for discussing the disparities in spatial and temporal scales of OM transfer, changes in environmental conditions, ecosystem connectivity, and microbial–substrate interactions. The potential relevance of priming effects in both terrestrial and aquatic systems is addressed. For terrestrial systems, we hypothesize that the interplay between the influx of OM, its corresponding elemental composition, and the elemental demand of the microbial communities may alleviate spatial and metabolic thresholds. In comparison, substrate level OM dynamics may be substantially different in aquatic systems due to matrix effects that accentuate the role of abiotic conditions, substrate quality, and microbial community dynamics. We highlight the disproportionate impact anthropogenic activities can have on OM cycling across the landscape. This includes reversing natural OM flows through the landscape, disrupting ecosystem connectivity, and nutrient additions that cascade across the landscape. This knowledge is crucial for a better understanding of OM cycling in a landscape context, in particular since terrestrial and aquatic compartments may respond differently to the ongoing changes in climate, land use, and other anthropogenic interferences.

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera’s automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems.

The original version of this Article contained an error in the first sentence of the Acknowledgements section, which incorrectly referred to the Estonian Research Council grant identifier as “PUTJD618”. The correct version replaces the grant identifier with “PUTJD619”. This has been corrected in both the PDF and HTML versions of the Article.

The effects of clear-cutting on the ammonia-oxidising bacterial community were studied in the soil of limed and non-limed spruce forest plots located in the central part of southern Sweden. The communities were studied using denaturing gradient gel electrophoresis (DGGE) profiling after polymerase chain reaction (PCR) amplification from total DNA with primers reported to be specific for ß-subgroup ammonia-oxidising bacteria. The bands on the DGGE were sequenced and each unique sequence was interpreted as representing one ammonia-oxidising population. The relative abundance of each population was determined by measuring the fluorescence of the respective DGGE bands. In both limed and non-limed soil, the same two Nitrosospira populations were found, one belonging to cluster 2 (NScl2) and one to cluster 4 (NScl4). However, while NScl4 first appeared a year after the clear-cutting in the non-limed plot, it was present both before and after the cutting in the limed plot. Irrespective of previous liming, clear-cutting caused a shift in the ammonia-oxidiser community, from dominance by the NScl2 population to a community with approximately equal relative abundance of NScl2 and NScl4. In both plots the total size of the community increased after clear-cutting (based on increased DGGE band intensity), most likely due to increased NH 4 + availability, but the growth response was faster in the limed plot. Hence, the prior liming increased the responsiveness of the ammonia-oxidisers to the changes caused by cutting. This is the first study to report the effects of clear-cutting on the ammonia-oxidising community, and the results show a clear correlation between increased potential nitrification and a shift in the ammonia-oxidiser community. [ABSTRACT FROM AUTHOR]