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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.

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.

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 (N 2 O) to the atmosphere. Soils are a large contributor to N 2 O emissions on a global scale. The aim of this study was to investigate the consequences of long-term nutrient optimization fertilization on N 2 O emissions in a boreal forest in Northern Sweden. Field N 2 O flux measurements were conducted during 2 years with manual and automatic chambers, as well as gas probes in the snow. The N 2 O 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, N 2 O emissions from fertilized plots were about twice the control, which could be explained by changes in the soil carbon-to-nitrogen ratio.

This paper presents an integrated analysis of organic carbon (C) pools in soils and vegetation, within-ecosystem fluxes and net ecosystem exchange (NEE) in three 40-year old Norway spruce stands along a north-south climatic gradient in Sweden, measured 2001-2004. A process-orientated ecosystem model (CoupModel), previously parameterised on a regional dataset, was used for the analysis. Pools of soil organic carbon (SOC) and tree growth rates were highest at the southernmost site (1.6 and 2.0-fold, respectively). Tree litter production (litterfall and root litter) was also highest in the south, with about half coming from fine roots (<1 mm) at all sites. However, when the litter input from the forest floor vegetation was included, the difference in total litter input rate between the sites almost disappeared (190-233 g C m⁻² year⁻¹). We propose that a higher N deposition and N availability in the south result in a slower turnover of soil organic matter than in the north. This effect seems to overshadow the effect of temperature. At the southern site, 19% of the total litter input to the O horizon was leached to the mineral soil as dissolved organic carbon, while at the two northern sites the corresponding figure was approx. 9%. The CoupModel accurately described general C cycling behaviour in these ecosystems, reproducing the differences between north and south. The simulated changes in SOC pools during the measurement period were small, ranging from -8 g C m⁻² year⁻¹ in the north to +9 g C m⁻² year⁻¹ in the south. In contrast, NEE and tree growth measurements at the northernmost site suggest that the soil lost about 90 g C m⁻² year⁻¹.

We analysed the effect of the 2018 European drought on greenhouse gas (GHG) exchange of five North European mire ecosystems. The low precipitation and high summer temperatures in Fennoscandia led to a lowered water table in the majority of these mires. This lowered both carbon dioxide (CO 2 ) uptake and methane (CH 4 ) emission during 2018, turning three out of the five mires from CO 2 sinks to sources. The calculated radiative forcing showed that the drought-induced changes in GHG fluxes first resulted in a cooling effect lasting 15–50 years, due to the lowered CH 4 emission, which was followed by warming due to the lower CO 2 uptake. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

The emissions of the greenhouse gas nitrous oxide (N 2 O) were measured from a non nitrogen fertilized carrot ( Daucus carota ssp. sativa ) field on an organic soil in Sweden during one cropping and post-harvest season. The cumulative emission during the measuring period of 149 days was 41 (±2.8) kg N 2 O ha −1 . Dividing the measuring period into a cropping and a post-harvest period revealed that the presence of carrots strongly stimulated N 2 O emissions, as the emission during the cropping period was one order of magnitude higher compared to the post-harvest period. The N 2 O emission from the carrot field were higher than fluxes reported from cereal crop and grass production, but in the same order as reported fluxes from vegetable cropping on organic soils. In conclusion, our results indicate that the cultivation of root vegetable, such as carrots, on organic soil can be a high point source for N 2 O emissions.

Recent investigations have highlighted the relative importance of the winter season for emissions of N 2O from boreal soils. However, our understanding of the processes and environmental controls regulating these emissions is fragmentary. Therefore, we investigated the potential for, and relative importance of, N 2O formation at temperatures below 0 °C in laboratory experiments involving incubations of a Swedish boreal forest soil. Our results show that frozen soils have a high potential for N 2O formation and subsequent emission. Net N 2O production rates at −4 °C equaled those observed at +10 to +15 °C at moisture contents >60% of the soil's water-holding capacity. The source of this N 2O was found to be denitrification occurring in anoxic microsites in the frozen soil and temperature per se did not control the denitrification rates at temperatures around 0 °C. Furthermore, both net nitrogenmineralisation and nitrification were observed in the frozen soil samples. Based on these findings we propose a conceptual model for the temperature response of N 2O formation in soils at low temperatures.

In this study, we use a data assimilation framework based on the adaptive Markov chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH.sub.4 eddy-covariance flux observations from 14 different natural boreal, temperate, and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH.sub.4 flux output against independent CH.sub.4 flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH.sub.4 fluxes from boreal, temperate, and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH.sub.4 fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH.sub.4 emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH.sub.4 emission estimation using the optimised model resulted in 28.16 Tg C yr.sup.-1 and for regions above 60 ° N it resulted in 7.46 Tg C yr.sup.-1.

In this study, we use a data assimilation framework based on the adaptive Markov chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH4 eddy-covariance flux observations from 14 different natural boreal, temperate, and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH4 flux output against independent CH4 flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from boreal, temperate, and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH4 emission estimation using the optimised model resulted in 28.16 Tg C yr−1 and for regions above 60 ° N it resulted in 7.46 Tg C yr−1.

In this study, we use a data assimilation framework based on the adaptive Markov chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH4 eddy-covariance flux observations from 14 different natural boreal, temperate, and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH4 flux output against independent CH4 flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from boreal, temperate, and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH4 emission estimation using the optimised model resulted in 28.16 Tg C yr−1 and for regions above 60 ° N it resulted in 7.46 Tg C yr−1.