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Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.

It is projected that forest disturbances, such as insect outbreaks, will have an increasingly negative impact on forests with a warmer climate. These disturbance events can have a substantial impact on forests' ability to absorb atmospheric CO2, and may even turn forests from carbon sinks into carbon sources; hence, it is important to develop methods both to monitor forest disturbances and to quantify the impact of these disturbance events on the carbon balance. In this study we present a method to monitor insect-induced defoliation in a subarctic birch forest in northern Sweden, and to quantify the impact of these outbreaks on gross primary productivity (GPP). Since frequent cloud cover in the study area requires data with high temporal resolution and limits the use of finer spatial resolution sensors such as Landsat, defoliation was mapped with remote sensing data from the MODIS sensor with 250 m × 250 m spatial resolution. The impact on GPP was estimated with a light use efficiency (LUE) model that was calibrated with GPP data obtained from eddy covariance (EC) measurements from 5 years with undisturbed birch forest and 1 year with insect-induced defoliation. Two methods were applied to estimate the impact on GPP: (1) applying a GPP reduction factor derived from EC measured GPP to estimate GPP loss, and (2) running a LUE model for both undisturbed and defoliated forest and deriving the differences in modelled GPP. In the study area of 100 km2 the results suggested a substantial setback to the carbon uptake: an average decrease in regional GPP over the three outbreak years (2004, 2012, and 2013) was estimated to 15 ± 5 Gg C yr−1, compared to the mean regional GPP of 40 ± 12 Gg C yr−1 for the 5 years without defoliation, i.e. 38 %. In the most severe outbreak year (2012), 76 % of the birch forests were defoliated, and annual regional GPP was merely 50 % of GPP for years without disturbances. The study has generated valuable data on GPP reduction, and demonstrates a potential for mapping insect disturbance impact over extended areas.

A large amount of organic carbon is stored in high-latitude soils. A substantial proportion of this carbon stock is vulnerable and may decompose rapidly due to temperature increases that are already greater than the global average. It is therefore crucial to quantify and understand carbon exchange between the atmosphere and subarctic/arctic ecosystems. In this paper, we combine an Arctic-enabled version of the process-based dynamic ecosystem model, LPJ-GUESS (version LPJG-WHyMe-TFM) with comprehensive observations of terrestrial and aquatic carbon fluxes to simulate long-term carbon exchange in a subarctic catchment at 50 m resolution. Integrating the observed carbon fluxes from aquatic systems with the modeled terrestrial carbon fluxes across the whole catchment, we estimate that the area is a carbon sink at present and will become an even stronger carbon sink by 2080, which is mainly a result of a projected densification of birch forest and its encroachment into tundra heath. However, the magnitudes of the modeled sinks are very dependent on future atmospheric CO2 concentrations. Furthermore, comparisons of global warming potentials between two simulations with and without CO2 increase since 1960 reveal that the increased methane emission from the peatland could double the warming effects of the whole catchment by 2080 in the absence of CO2 fertilization of the vegetation. This is the first process-based model study of the temporal evolution of a catchment-level carbon budget at high spatial resolution, including both terrestrial and aquatic carbon. Though this study also highlights some limitations in modeling subarctic ecosystem responses to climate change, such as aquatic system flux dynamics, nutrient limitation, herbivory and other disturbances, and peatland expansion, our study provides one process-based approach to resolve the complexity of carbon cycling in subarctic ecosystems while simultaneously pointing out the key model developments for capturing complex subarctic processes.