The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

Accurate delineation of spatiotemporal variations in ground surface soil freeze and thaw (F/T) states is essential to appraise many geoscience issues, such as the hydrological circulation and land surface-atmosphere feedbacks. Recently, an Improved Dual-index algorithm (DIA) method was proposed by accounting for the influence of soil moisture variations on the discrimination accuracy with passive microwave remote sensing (RS) data products. Compared with the original DIA, the Improved DIA method has proven to be a more practical approach on surface soil F/T states discrimination. However, the method has only been applied and verified in cold regions of high-altitude (e.g., Tibetan Plateau), it's applicability and effectiveness in the cold areas in mid-high latitudes, where the geographic and climatic conditions are quite different, yet remained to be further explored. The present study investigated the feasibility of using AMSR-E (the Advanced Microwave Scanning Radiometer-EOS) and AMSR2 (the second Advance Microwave Scanning Radiometer) passive microwave RS data products to discriminate the F/T states of the ground surface for a long period from 2002 to 2019 by means of the Improved DIA method over a typical mid-high latitude cold region of Northeastern China. Seasonal variation characteristics of soil moisture in mid-high latitude areas were similar with those in high-altitude areas, even though the spatial heterogeneity of soil water content was significant in different regions. Discriminating surface soil F/T states with the Improved DIA method derived overall discriminating accuracy of about 91.6% in the study area, which demonstrated excellent feasibility of the Improved DIA method in mid-high latitude cold regions. The mapping results shown surface soil F/T cycle in Northeastern China responding to climate change was examined from the perspective of regional average, both the proportion of frozen soil area and frozen days showed significant decreasing trends continuously with differed quite spatially. The discriminating accuracy of the Improved DIA method was found to be lower in plain areas with dense populations and large farmland areas compared to mountainous areas when human activities were not taken into consideration, as quantifying human activities can be challenging. The Improved DIA method has been well verified in both high-altitude and high-latitude regions; it has great potential in global scale research.

The freezing front depth (z(ff)) of annual freeze-thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because z(ff) is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual z(ff) with the diurnal amplitude variation of L-band brightness temperature (?T-B) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (z(tf)) variation, which leads further to ?T-B; second, ?T-B can be captured by an L-band radiometer; third, z(tf) and z(ff) are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and T-B observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ?T-B can be precisely estimated from z(tf) during the daytime; second, the decreasing of z(tf) is linearly related to the increase of z(ff) with the Stefan equation; third, the accuracy of retrieved z(ff) is about 5-25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band T-B data in cryosphere/meteorology and construct global freezing depth dataset in the future.

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China's three main stable snow cover regions during the past 40 years (1981-2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China's snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6 similar to 0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.

Permafrost monitoring using remote sensing techniques is an effective approach at present. Permafrost mostly occurs below the land surface, which limits permafrost monitoring by optical remote sensing. Considering the specific hydrothermal relations between permafrost and its active layer, we developed a permafrost monitoring and classification method that integrated the ground surface soil freeze/thaw states determined by the dual-index algorithm (DIA) and the permafrost classification method based on thermal stability. The modified frost index was introduced into the method as a link between the DIA and the permafrost classification method. Northeastern China was selected to establish and verify the proposed method and to examine the changes in regional permafrost against the background of global warming from 2002 to 2017. The results showed that the ground surface soil freeze/thaw states were significantly correlated with the permafrost distribution. The spatial continuity of permafrost and its sensitivity to climate change could be effectively reflected by the modified frost index. The proposed method had a high accuracy with a classification error smaller than 3%, compared with static permafrost maps. Moreover, the proportion of permafrost decreased from 29% at the beginning of the 21st century to 22.5% at present in northeastern China over the study period. The southern permafrost boundary in the study area generally moved northward approximately 25-75 km. Additionally, the method was applied to the Northern Hemisphere (30 degrees N - 90 degrees N), which demonstrated its effectiveness and extended applicability.

The upper Nu-Salween River basin in the Tibetan Plateau is mainly covered with seasonal frozen soils. We used daily surface freeze-thaw states, detected from Special Sensor Microwave/Imager (SSM/I) daily brightness temperature data, to analyze the variations in surface freeze-thaw states and the relationship with air temperature. We also examined baseflow to explore the influences of interannual variations in the start time of soil freezing on hydrological processes. The results showed that (1) interannual air temperature fluctuations led to differences in the area and start time of surface freezing. When surface soil froze, flow was mainly dependent on existing groundwater storage. (2) The interannual variation in the surface freezing time directly affected the flow generation processes. When soil water froze and remained in the frozen layer, it was hard to generate surface flow, so flow mainly consisted of baseflow, causing the proportion of the baseflow in the total flow to gradually increase. (3) The surface freeze-thaw states obtained from the passive microwave remote sensing data may be applied to support further research on the hydrological impacts of freeze-thaw cycle variations in plateau mountain basins.