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Abstract As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Soil pH significantly impacts microbial activity and community assembly, which in turn determines the temperature sensitivity (Q10) of soil respiration. Due to the high soil acidification in China, it is necessary to understand how soil acidification impacts Q10. Here, the Q10 of soil respiration was examined in a long-term field experiment (1982–present) with different soil pH caused by fertilization management. In this experiment, we selected treatments with neutral pH: (1) no crops and fertilization (CK); (2) crops without fertilization (NF); low pH with (3) crops with chemical fertilization (NPK); and (4) crops with chemical fertilization combined with wheat straw incorporation (WS). Under natural soil temperature changes, we observed that soil acidification lowered the Q10 value of soil respiration. Considering only temperature changes, the Q10 of soil respiration was strongly associated with microbial community composition, alpha diversity, and soil ammonium nitrogen. Considering the interaction between soil pH and temperature, warming strengthened the negative effect of soil pH on the Q10 of soil respiration, and the pathway through which soil pH mediated Q10 included not only microbial community composition, alpha diversity, and biomass but also the soil’s available phosphorus. This work enhanced our insights into the relationships between Q10, temperature, and soil pH by identifying important microbial properties and key soil environmental factors.

Stable carbon (C) and nitrogen (N) isotopes (13C and 15N) in water-extractable organic matter (WEOM) derived from air-dried soils may be applicable to elucidate the microbial decomposition of soil organic matter (SOM), which is crucial in terrestrial C cycles. A total of 40 soil samples were collected from a depth of 0–6 cm from a temperate broadleaved forest in Japan with vegetation succession from grassland approximately 150 years ago. Those soil samples were air-dried before the water extraction process and organic matter analysis. The C and N concentrations of WEOM were

Agriculture in the boreal and Arctic regions is perceived as marginal, low intensity and inadequate to satisfy the needs of local communities, but another perspective is that northern agriculture has untapped potential to increase the local supply of food and even contribute to the global food system. Policies across northern jurisdictions target the expansion and intensification of agriculture, contextualized for the diverse social settings and market foci in the north. However, the rapid pace of climate change means that traditional methods of adapting cropping systems and developing infrastructure and regulations for this region cannot keep up with climate change impacts. Moreover, the anticipated conversion of northern cold-climate natural lands to agriculture risks a loss of up to 76% of the carbon stored in vegetation and soils, leading to further environmental impacts. The sustainable development of northern agriculture requires local solutions supported by locally relevant policies. There is an obvious need for the rapid development of a transdisciplinary, cross-jurisdictional, long-term knowledge development, and dissemination program to best serve food needs and an agricultural economy in the boreal and Arctic regions while minimizing the risks to global climate, northern ecosystems and communities.

The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000–2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e. flooding events caused by abnormal precipitation) during 2005–2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June–August at Elgeeii ( p < 0.05), whereas no significant trends were found at Spasskaya Pad ( p > 0.05). NDVI anomalies in May and June–August at Elgeeii were significantly associated with precipitation or temperature depending on the season ( p < 0.05), whereas no significant relationships were found at Spasskaya Pad ( p > 0.05). Thus, the 20 year NDVI trend and NDVI–temperature–precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.

Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training.

Plant phenology timings, such as spring green-up and autumn senescence, are essential state information characterizing biological responses and terrestrial carbon cycles. Current efforts for the in situ reflectance measurements are not enough to obtain the exact interpretation of how seasonal spectral signature responds to phenological stages in boreal evergreen needleleaf forests. This study shows the first in situ continuous measurements of canopy scale (overstory + understory) and understory spectral reflectance and vegetation index in an open boreal forest in interior Alaska. Two visible and near infrared spectroradiometer systems were installed at the top of the observation tower and the forest understory, and spectral reflectance measurements were performed in 10 min intervals from early spring to late autumn. We found that canopy scale normalized difference vegetation index (NDVI) varied with the solar zenith angle. On the other hand, NDVI of understory plants was less sensitive to the solar zenith angle. Due to the influence of the solar geometry, the annual maximum canopy NDVI observed in the morning satellite overpass time (10–11 am) shifted to the spring direction compared with the standardized NDVI by the fixed solar zenith angle range (60−70°). We also found that the in situ NDVI time-series had a month-long high NDVI plateau in autumn, which was completely out of photosynthetically active periods when compared with eddy covariance net ecosystem exchange measurements. The result suggests that the onset of an autumn high NDVI plateau is likely to be the end of the growing season. In this way, our spectral measurements can serve as baseline information for the development and validation of satellite-based phenology algorithms in the northern high latitudes.

A fibre-optic DTS (distributed temperature sensing) system using Raman-scattering optical time domain reflectometry was deployed to monitor a boreal forest research site in the interior of Alaska. Surface temperatures range between -40°C in winter to 30°C in summer at this site. In parallel experiments, a fibre-optic cable sensor system (multi-mode, GI50/125, dual core; 3.4mm) monitored at high-resolution (0.5-metre intervals every 30minutes) ground surface across the landscape and, at several points a high resolution vertical profile from the upper subsurface to a meter above. The total cable ran 2.7km with about 2.0km monitoring a horizontal surface path. Sections of the cable sensor were deployed in vertical coil configurations (1.2m high) to measure vertical temperature profiles from the ground up at 5-mm intervals. Measurements were collected continuously over a two year interval from October, 2012 to October, 2014. Vegetation of the overall site (Poker Flat Research Range) consists primarily of black spruce underlain by permafrost. Land cover types within the study area were classified into six descriptive categories: relict thermokarst lake, open moss, shrub, deciduous forest, sparse conifer forest, and dense conifer forest. The horizontal temperature data exhibited spatio-temporal patterns within the observed diurnal and seasonal variation. Differences in snow pack evolution and insulation effects co-varied with the land cover types. The apparatus used to monitor vertical temperature profiles generated high-resolution (c. 5mm) data for air column, snow cover, and ground surface. This research also identified several technical challenges of deploying and maintaining a DTS system in sub-arctic environments. [ABSTRACT FROM AUTHOR]