Accurate soil thermal conductivity (STC) data and their spatiotemporal variability are critical for the accurate simulation of future changes in Arctic permafrost. However, in-situ measured STC data remain scarce in the Arctic permafrost region, and the STC parameterization schemes commonly used in current land surface process models (LSMs) fail to meet the actual needs of accurate simulation of hydrothermal processes in permafrost, leading to considerable errors in the simulation results of Arctic permafrost. This study used the XGBoost method to simulate the spatial-temporal variability of the STC in the upper 5 cm active layer of Arctic permafrost during thawing and freezing periods from 1980 to 2020. The findings indicated STC variations between the thawing and freezing periods across different years, with values ranging from-0.4 to 0.28 W & sdot;m-1 & sdot;K-1. The mean STC during the freezing period was higher than that during the thawing period. Tundra, forest, and barren land exhibited the greatest sensitivity of STC to freeze-thaw transitions. This is the first study to explore the long-term spatiotemporal variations of STC in Arctic permafrost, and these findings and datasets can provide useful support for future research on Arctic permafrost evolution simulations.

The Qinghai-Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai-Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai-Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km(2), and there was a significant increasing trend (=322 km(2)/year, p < 0.01) during 2000-2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.