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Arctic terrestrial ecosystems are major global sources of methane (CH₄); hence, it is important to understand the seasonal and climatic controls on CH₄ emissions from these systems. Here, we report year-round CH₄ emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH₄ flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the “zero curtain” period, when subsurface soil temperatures are poised near 0 °C. The zero curtain may persist longer than the growing season, and CH₄ emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH₄ derived from aircraft data demonstrate the large spatial extent of late season CH₄ emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH₄ y⁻¹, ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH₄ emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.

Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE  ≤  0.06), PWV (R ≥ 0.91; RMSE  ≤  4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE  ≤  3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE  ≤  0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.

Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts. Plain Language Summary Small water bodies (<0.01 km2) are an important driver of many Earth system processes. Despite their importance, many existing water mapping products have difficulty detecting these small water features and their seasonal changes in surface area. We used deep learning and high‐resolution (3 m) satellite imagery to map and monitor seasonal changes in the areal extent of lakes and small ponds across four regions in Alaska. The resulting water maps accounted for considerably more water coverage than existing products. The maps also effectively tracked widespread seasonal changes in pond and lake area that were not previously identified. This demonstrates the importance of monitoring surface water at high spatial resolutions and across seasons. Key Points Deep learning and 3 m resolution satellite imagery from Planet Labs can detect and track ponds and lakes >0.0001 km2 Total surface area for ponds (<0.01 km2) in boreal forest and tundra environments can vary by 20%–40% throughout an individual season Ponds can contribute to a broad range (8%–37%) of total methane emissions from lakes and ponds in northern boreal forest and tundra

Warming of northern high latitude regions (NHL, > 50 °N) has increased both photosynthesis and respiration which results in considerable uncertainty regarding the net carbon dioxide (CO 2 ) balance of NHL ecosystems. Using estimates constrained from atmospheric observations from 1980 to 2017, we find that the increasing trends of net CO 2 uptake in the early-growing season are of similar magnitude across the tree cover gradient in the NHL. However, the trend of respiratory CO 2 loss during late-growing season increases significantly with increasing tree cover, offsetting a larger fraction of photosynthetic CO 2 uptake, and thus resulting in a slower rate of increasing annual net CO 2 uptake in areas with higher tree cover, especially in central and southern boreal forest regions. The magnitude of this seasonal compensation effect explains the difference in net CO 2 uptake trends along the NHL vegetation- permafrost gradient. Such seasonal compensation dynamics are not captured by dynamic global vegetation models, which simulate weaker respiration control on carbon exchange during the late-growing season, and thus calls into question projections of increasing net CO 2 uptake as high latitude ecosystems respond to warming climate conditions. The northern high latitude permafrost region has been an important contributor to the carbon sink since the 1980s. A new study finds that as tree cover increases, respiratory CO2 loss during late-growing season offsets photosynthetic CO2 uptake and leads to a slower rate of increasing annual net CO2 uptake.

Rain-on-snow (ROS) events are characterized by liquid precipitation or condensation onto snow surfaces that can lead to snowmelt and the formation of ice layers. ROS events can directly alter the physical structure and thermal properties of the snowpack, leading to rapid melting and runoff-induced flooding, reduced snow insulation, and permafrost degradation. However, tracking ROS events and regional trends remain uncertain due to limited ground measurements and lack of long-term satellite ROS observations representing vast and remote boreal-Arctic landscapes. We quantified ROS dynamics over Alaska by developing a daily 6 km resolution ROS record using an established gradient ratio polarization approach and 30 year (1988–2017) satellite observations from the Special Sensor Microwave Imager/Image Sounder (SSMI/S) sensors. The data record captured well-documented ROS events and showed high consistency (R 0.94) with alternative ROS predictions from the Advanced Microwave Scanning Radiometer-EOS/2 sensors. Analysis showed an overall increasing ROS frequency with significant trends mainly identified during early winter. Notable rises in ROS frequency were also detected in mid-to-high elevation ranges (>400 m above sea level (ASL)), while this increase diminished at higher elevations (>1000 m ASL). Our analysis further confirmed that the warming climate plays a fundamental role in driving these ROS events, with significantly positive correlations between ROS frequency and air temperature. However, the significant correlations did not extend to the Eastern and Western Gulf climate zones of southern Alaska, where the ROS retrievals were likely affected by coastal ocean contamination of SSMI/S observations.

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10s of kilometers. Hence a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine- learning (ML) and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing ab- solute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in-situ soil moisture measurements for the Australian Yanco region, while preserving the high sampling frequency (1-3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses.

Permafrost thaw is transforming boreal forests into mosaics of wetlands and drier uplands. Topographic controls on hydrological and ecological conditions impact methane (CH4) fluxes, contributing to uncertainty in local and regional CH4 budgets and underlying drivers. The objective of this study was to explore CH4 fluxes and their drivers in a transitioning boreal forest-fen ecosystem (Goldstream Valley, Alaska, USA). This landscape is characterized by thawing discontinuous permafrost and heterogeneous mosaics of fens, collapse-scar channels, and small mounds of permafrost soils. From a survey in July 2021, observed chamber CH4 fluxes included fen areas with intermediate to very high emissions (29.8–635.3 mg CH4 m−2 d−1), clustered locations with CH4 uptake (−2.11 to −0.7 mg CH4 m−2 d−1), and three anomalous emission hotspots (342.4–772.4 mg CH4 m−2 d−1) that were located near samples with lower emissions. Some surface and near-surface variables partially explained the spatial variation in CH4 flux. Log-transformed CH4 flux had a positive linear relationship with soil moisture at 20 cm depth (R2 = 0.31, p-value < 1e-5) and negative linear relationships with microtopography (R2 = 0.13, p-value < 0.006) and slope (R2 = 0.28, p-value < 2e-5). Methane emissions generally occurred in flat, wet, graminoid-dominated fens, whereas CH4 uptake occurred on permafrost mounds dominated by feather mosses and woody vegetation. However, the CH4 hotspots occurred on drier, slightly sloped locations with low or undetectable near-surface methanogen abundance, suggesting that CH4 was produced in deeper soils. When the hotspot samples were omitted, log-transformed CH4 flux had a positive linear relationship with near-surface methanogen abundance (R2 = 0.29, p-value = 0.0023), and stronger linear relationships with soil moisture, slope, and soil macronutrient concentrations. Our findings suggest that some CH4 emission hotspots could arise from CH4 in deep taliks. The inference that methanogenesis occurs in deep taliks was strengthened by the identification of intrapermafrost taliks across the study area using low-frequency geophysical induction. This study assesses surface spatial heterogeneity in the context of subsurface permafrost conditions and highlights the complexity of CH4 flux patterns in transitioning forest-wetland ecosystems. To better inform regional CH4 budgets, further research is needed to understand the spatial distribution of terrestrial CH4 hotspots and to resolve their surface, near-surface, and subsurface drivers.

The high latitudes cover ∼20% of Earth's land surface. This region is facing many shifts in thermal, moisture and vegetation properties, driven by climate warming. Here we leverage remote sensing and climate reanalysis records to improve understanding of changes in ecosystem indicators. We applied non‐parametric trend detections and Getis‐Ord Gi* spatial hotspot assessments. We found substantial terrestrial warming trends across Siberia, portions of Greenland, Alaska, and western Canada. The same regions showed increases in vapor pressure deficit; changes in precipitation and soil moisture were variable. Vegetation greening and browning were widespread across both continents. Browning of the boreal zone was especially evident in autumn. Multivariate hotspot analysis indicated that Siberian ecoregions have experienced substantial, simultaneous, changes in thermal, moisture and vegetation status. Finally, we found that using regionally‐based trends alone, without local assessments, can yield largely incomplete views of high‐latitude change.

The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPRs). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with available in situ ALT network measurements ( n = 2966 site-years), we produced annual estimates of ALT at 1 km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy ( R 2 = 0.97) and low bias when compared with in situ ALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm yr −1 [25%–75% quantile: (−0.035, 0.204) cm yr −1 ]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends.