Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

Permafrost is a sub-ground phenomenon and therefore cannot be directly observed from space. It is an Essential Climate Variable and associated with climate tipping points. Multi-annual time series of permafrost ground temperatures can be, however, derived through modelling of the heat transfer between atmosphere and ground using landsurface temperature, snow- and landcover observations from space. Results show that the northern hemisphere permafrost ground temperatures have increased on average by about one degree Celsius since 2000. This is in line with trends of permafrost proxies observable from space: surface water extent has been decreasing across the Arctic; the landsurface is subsiding continuously in some regions indicating ground ice melt; hot summers triggered increased subsidence as well as thaw slumps; rock glaciers are accelerating in some mountain regions. The applicability of satellite data for permafrost proxy monitoring has been demonstrated mostly on a local to regional scale only. There is still a lack of consistency of acquisitions and of very high spatial resolution observations. Both are needed for implementation of circumpolar monitoring of lowland permafrost. In order to quantify the impacts of permafrost thaw on the carbon cycle, advancement in wetland and atmospheric greenhouse gas concentration monitoring from space is needed.

Permafrost in Northeastern China has significantly degraded due to global warming, deforestation and urbani-zation in the last few decades. The frost heave and thaw subsidence induced by freeze-thaw cycles of deep seasonal frozen ground have caused serious damage to infrastructures. The Shiwei-Labudalin (Shi-La) Highway is an important infrastructure connecting Shiwei town and Labudalin town of Argun city, Inner Mongolia, which passes through the areas covered by deep seasonal frozen ground or isolated patchy permafrost. In this paper, we mapped the long-term linear displacement trend and amplitude of seasonal displacement of the Shi-La Highway and its nearby areas, with an ascending Sentinel-1 dataset acquired from September 2016 to April 2020. Seasonal displacement amplitudes of 5-20 mm are widely detected in low-lying areas (e.g., the basin of the Gen and Derbugan rivers). The time lags between frozen ground displacement and temperature variations generally range from 10 to 80 days while larger values of 100-120 days caused by soil moisture or land cover difference are also observed. Linear creep displacement rates greater than-20 mm/yr are detected on mountainous slopes and sections of the Shi-La Highway in the line-of-sight (LOS) direction. Our results provide a method for evaluating highway stability in cold regions, which is helpful to highway route selection and design in Northeastern China.

The prospect of precipitation is of great significance to the distribution of industry and agriculture in Northwest China. The cycle characteristics of temperature and precipitation in the Qilian Mountains were identified by complex Morlet wavelet analysis and were simulated with sine functions. The results indicate that the main cycle of 200 years modulates the variations of temperature and precipitation over the past 2000 years and that cycle simulations fluctuate around the long-term trend. The temperature in the Qilian Mountains exhibits an obvious upward trend during the period 1570-1990 AD, while the precipitation trend shows a slight increase. The wet-island moisture pattern of the Qilian Mountains may be responsible for this. The moisture of the Qilian Mountains is principally sourced from the evapotranspiration of adjacent arid and semi-arid areas and is controlled by regional climate. The precipitation is close to the relative maximum and is at the positive phase of main cycle. It may not be beyond 400 mm in the next 200-year cycle, and the increment of precipitation might result from regional climate change.

The evolution of the average freezing depth and maximum freezing depth of seasonal frozen soil and their correlations with the average winter half-year temperature in Heilongjiang Province in China are analyzed. Linear regression, the Mann-Kendall test, and kriging interpolation are applied to freezing depth data from 20 observation stations in Heilongjiang Province from 1972 to 2016 and daily average temperature data from 34 national meteorological stations collected in the winters of 1972-2020. The results show that the average freezing depth decreases at a rate of 4.8 cm (10 yr)(-1) and that the maximum freezing depth decreases at a rate of 10.1 cm (10 yr)(-1). The winter half-year average temperature generally shows a fluctuating upward trend in Heilongjiang Province, increasing at a rate of 0.3 degrees C (10 yr)(-1). The correlations between the average and maximum freezing depths and the winter half-year average temperature are -0.53 and -0.49, respectively. For every 1 degrees C increase in the average temperature during the winter half of the year, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm. The average freezing depth sequence mutated in 1987, and the maximum freezing depth sequence mutated in 1988. The average temperature in the winter half-year displayed multiple abrupt changes from 1972 to 2020. The spatial variations in the average and maximum freezing depths are basically consistent with those in the average winter half-year temperature. These research results provide a theoretical basis for the design and site selection of hydraulic structures in cold areas and for regional development and agricultural planning. Significance StatementThe freeze-thaw balance in the frozen soil environment has been disrupted in recent years, and various degrees of degradation have occurred in the frozen soil. The degradation of frozen soil will further aggravate the greenhouse effect, which in turn will affect the accumulation of water in the soil and will have a significant impact on local agricultural production. This article uses Heilongjiang Province in China as an example. The results show that 1) the temperature in the winter half-year has exhibited an upward trend in recent years, 2) the temperature in the winter half-year has a considerable impact on the frozen soil environment, and 3) the response of the spatial distribution of frozen soil to temperature changes in the winter half-year is revealed.

InSAR time series of surface deformation from 16 yr of Envisat (2003-2011) and Sentinel-1 (2014-2019) ESA satellite radar measurements have been constructed to characterise spatial and temporal dynamics of ground deformation over an 80,000 km(2) area in the permafrost of the northeastern Tibetan Plateau. The regional deformation maps encompass various types of periglacial landforms and show that seasonal thaw effects are controlled by the sediment type and local topography. High seasonal ground movements are concentrated on shallow slopes and poor-drainage areas in unconsolidated, frost-susceptible and fine-grained sediments within glacier outwash plains, braided stream plains, alluvial deposits or floodplains. Fast subsidence due to thaw settlement takes place during June/July while frost heave is intense during December/January when two-sided freezing of pore water under pressure causes prolonged ice segregation near the permafrost table. The analysis reveals pervasive subsidence of the ground of up to similar to 2 cm/yr, and increasing by a factor of 2 to 5 from 2003 to today, in high-relief and well-drained areas. The findings suggest that seasonal thaw increasingly affects ice-rich layers at the permafrost table, as well as high-rates of widespread mass movements of non-consolidated sediments, the latter amplified by an increase of effects from frost heave/thaw settlement. (C) 2020 Elsevier B.V. All rights reserved.

The early twentieth-century warming (EW; 1910-45) and the mid-twentieth-century cooling (MC; 1950-80) have been linked to both internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after AD 1850. Instead, virtually all (97%-98%) of the global low-frequency variability (>30 years) can be explained by external forcing. We find similarly high percentages of explained variance for interhemispheric and land-ocean temperature evolution. Three key aspects are identified that underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic multidecadal variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally forced and internally generated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead [the North Atlantic Variability Index (NAVI)]. Our associated best estimate for the transient climate response (TCR) is 1.57 K (+/- 0.70 at the 5%-95% confidence level).

The discontinuous permafrost zone is one of the world's most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.

Snow cover is the most common upper boundary condition influencing the soil freeze-thaw process in the black soil farming area of northern China. Snow is a porous dielectric cover, and its unique physical properties affect the soil moisture diffusion, heat conduction, freezing rate and other variables. To understand the spatial distribution of the soil water-heat and the variable characteristics of the critical depth of the soil water and heat, we used field data to analyze the freezing rate of soil and the extent of variation in soil water-heat in a unit soil layer under bare land (BL), natural snow (NS), compacted snow (CS) and thick snow (TS) treatments. The critical depth of the soil water and heat activity under different snow covers were determined based on the results of the analysis, and the variation fitting curve of the difference sequences on the soil temperature and water content between different soil layers and the surface 5-cm soil layer were used to verify the critical depth. The results were as follows: snow cover slowed the rate of soil freezing, and the soil freezing rate under the NS, CS and TS treatments decreased by 0.099 cm/day, 0.147 cm/day and 0.307 cm/day, respectively, compared with that under BL. In addition, the soil thawing time was delayed, and the effect was more significant with increased snow cover. During freeze-thaw cycles, the extent of variation in the water and heat time series in the shallow soil was relatively large, while there was less variation in the deep layer. There was a critical stratum in the vertical surface during hydrothermal migration, wherein the critical depth of soil water and heat change gradually increased with increasing snow cover. The variance in differences between the surface layer and both the soil water and heat in the different layers exhibited steady-rising-steady behavior, and the inflection point of the curve is the critical depth of soil freezing and thawing. This critical layer is a demarcation point between frozen soil and non-frozen soil, delineating the boundary between soil water and heat migration and non-migration. Furthermore, with increasing snow cover thickness and increasing density, the critical depth gradually increased.