Vegetation in the arctic tundra typically consists of a small-scale mosaic of plant communities, with species differing in growth forms, seasonality, and biogeochemical properties. Characterization of this variation is essential for understanding and modeling the functioning of the arctic tundra in global carbon cycling, as well as for evaluating the resolution requirements for remote sensing. Our objective was to quantify the seasonal development of the leaf-area index (LAI) and its variation among plant communities in the arctic tundra near Tiksi, coastal Siberia, consisting of graminoid, dwarf shrub, moss, and lichen vegetation. We measured the LAI in the field and used two very-high-spatial resolution multispectral satellite images (QuickBird and WorldView-2), acquired at different phenological stages, to predict landscape-scale patterns. We used the empirical relationships between the plant community-specific LAI and degree-day accumulation (0°C threshold) and quantified the relationship between the LAI and satellite NDVI (normalized difference vegetation index). Due to the temporal difference between the field data and satellite images, the LAI was approximated for the imagery dates, using the empirical model. LAI explained variation in the NDVI values well (R 2 adj. 0.42-0.92). Of the plant functional types, the graminoid LAI showed the largest seasonal amplitudes and was the main cause of the varying spatial patterns of the NDVI and the related LAI between the two images. Our results illustrate how the short growing season, rapid development of the LAI, yearly climatic variation, and timing of the satellite data should be accounted for in matching imagery and field verification data in the Arctic region.

Abstract. Arctic tundra ecosystems will play a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) and to characterize them, sampled 92 study plots for plant and soil attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images. We found that soils ranged from mineral soils in bare soil and lichen tundra LCTs to soils of high percentage of organic matter (OM) in graminoid tundra, bog, dry fen and wet fen. OM content of the top soil was on average 14 g dm−3 in bare soil and lichen tundra and 89 g dm−3 in other LCTs. Total moss biomass varied from 0 to 820 g m−2, total vascular shoot mass from 7 to 112 g m−2 and vascular leaf area index (LAI) from 0.04 to 0.95 among LCTs. In late summer, soil temperatures at 15 cm depth were on average 14 ∘C in bare soil and lichen tundra, and varied from 5 to 9 ∘C in other LCTs. On average, depth of the biologically active, unfrozen soil layer doubled from early July to mid-August. When contrasted across study plots, moss biomass was positively associated with soil OM % and OM content and negatively associated with soil temperature, explaining 14–34 % of variation. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but only explained 6–15 % of variation. NDVI captured variation in vascular LAI better than in moss biomass, but while this difference was significant with late season NDVI, it was minimal with early season NDVI. For this reason, soil attributes associated with moss mass were better captured by early season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. The LCT map we produced had low to moderate uncertainty in predictions for plant and soil properties except for moss biomass and bare soil and lichen tundra LCTs. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including OM % and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance, topography or a LCT map. Despite the general accuracy of landscape level predictions in our LCT approach, this indicates challenges in the spatial extrapolation of some of those vegetation and soil attributes that are relevant for the regional ecosystem and global climate models.

Abstract. The patterned microtopography of subarctic mires generates a variety of environmental conditions, and carbon dioxide (CO2) and methane (CH4) dynamics vary spatially among different plant community types (PCTs). We studied the CO2 and CH4 exchange between a subarctic fen and the atmosphere at Kaamanen in northern Finland based on flux chamber and eddy covariance measurements in 2017–2018. We observed strong spatial variation in carbon dynamics between the four main PCTs studied, which were largely controlled by water table level and differences in vegetation composition. The ecosystem respiration (ER) and gross primary productivity (GPP) increased gradually from the wettest PCT to the drier ones, and both ER and GPP were larger for all PCTs during the warmer and drier growing season 2018. We estimated that in 2017 the growing season CO2 balances of the PCTs ranged from −20 g C m−2 (Trichophorum tussock PCT) to 64 g C m−2 (string margin PCT), while in 2018 all PCTs were small CO2 sources (10–22 g C m−2). We observed small growing season CH4 emissions (< 1 g C m−2) from the driest PCT, while the other three PCTs had significantly larger emissions (mean 7.9, range 5.6–10.1 g C m−2) during the two growing seasons. Compared to the annual CO2 balance (−8.5 ± 4.0 g C m−2) of the fen in 2017, in 2018 the annual balance (−5.6 ± 3.7 g C m−2) was affected by an earlier onset of photosynthesis in spring, which increased the CO2 sink, and a drought event during summer, which decreased the sink. The CH4 emissions were also affected by the drought. The annual CH4 balance of the fen was 7.3 ± 0.2 g C m−2 in 2017 and 6.2 ± 0.1 g C m−2 in 2018. Thus, the carbon balance of the fen was close to zero in both years. The PCTs that were adapted to drier conditions provided ecosystem-level resilience to carbon loss due to water level drawdown.

Abstract. The patterned microtopography of subarctic mires generates a variety of environmental conditions, and carbon dioxide (CO2) and methane (CH4) dynamics vary spatially among different plant community types. We studied the CO2 and CH4 exchange between a subarctic fen and the atmosphere at Kaamanen in northern Finland based on flux chamber and eddy covariance measurements in 2017–2018. We observed strong spatial variation in carbon dynamics between the four main plant community types (PCTs) studied, which were largely controlled by water table level and differences in vegetation composition. The ecosystem respiration (ER) and gross primary productivity (GPP) increased gradually from the wettest PCT to the drier ones, and both ER and GPP were larger for all PCTs during the warmer and drier growing season 2018. We estimated that in 2017 the growing season CO2 balances of the PCTs ranged from −20 g C m−2 (Trichophorum tussock PCT) to 64 g C m−2 (string margin PCT), while in 2018 all PCTs were small CO2 sources (10–22 g C m−2). We observed small growing season CH4 emission sums (

Abstract:Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor's view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC-necessary for algorithm development and validation-are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1-0.3 (with the full range of 0-1).