The surface freezing-thawing cycle is an important indicator of global climate change. In current studies, more attention has been paid on the monitoring of daily freezing-thawing changes, but less on the intra-day freezing-thawing changes. While the intraday freeze-thaw cycles are more sensitive to climate change. Thus, in this study, the multi-temporal observations derived from DMSP F15, F16, F17 and F18 in the period of 2015.01.01~2016.12.31 were used to monitoring the intraday freezing and thawing varations. A unified discriminant equation for four kinds of satellites was established. The ground soil temperature measurements from four networks (Naqu, Maqu, Pali and Ali) in the Qinghai-Tibet Plateau were used for the validation. The results show that there is a strong consistency between the intraday frozen duration calculated based on the F/T discrimination results and the in-situ measurements, with a maximum standard error of 12.6 hours in Ali and a minimum standard error of 4.34 hours in Naqu.

In permafrost regions, the ground surface deformation is closely related to the ice-water phase transition process in active layer and underground ice. It is of great significance to carry out ground surface deformation detection research for understanding the development of permafrost in the Qinghai- Tibet Plateau. The InSAR technology has been proved to be an effective method for monitoring frozen soil deformation. However, due to the limitation of spatial coverage and revisit cycle of SAR data, few scholars had paid attention on the unstable permafrost regions with less ice content in the previous studies. Thus, a less ice content permafrost region loceted in Tuotuohe was taken as the study area to carry out surface deformation detection research based on SBAS-InSAR technology, and the field measurement was carried out to do the verification. The result showed that there was a good agreement between them, and the errors are 0.9mm, 2.6mm and 2.8mm respectively. The deformation detected by SBAS-InSAR method well reflects the frost heaving and thaw subsidence trend along with seasonal changes. Considering the small content of underground ice, this trend mainly reflects the seasonal freezing-thawing variation of the active layer. This study further confirms the applicability of InSAR technology in unstable permafrost regions.

This article reports on the development of a deep Q-learning approach to long-term energy collection in self-sustainable sensor networks, which consists of two static chargers (SCs) and one rechargeable mobile sensor (MS). In particular, we assume that the SCs can harvest energy from the ambient environment and charge the MS via electromagnetic (EM) radiation. As the energy harvesting (EH) process is random and the radiated energy fades over distance, the achievable energy by the MS at each slot demonstrates spatiotemporal dynamics in a certain area. Thus, we focus on the problem of trajectory optimization for an autonomous MS to maximize the long-term average achievable energy per slot from both chargers. Due to the inaccessible charger-side information, such as the EH profile and locations of SCs as well as the transmit power, we introduce deep Q-learning, a model-free reinforcement learning approach, based on which the MS can learn and optimize the moving trajectory by intelligently tracking the aggregated received EM signal without any other explicit external information. Simulation results show that the MS can identify the best energy collecting location and finally moves there along the learned trajectory. We also investigate the impact of system parameters, such as initial position and moving cost per unit distance on the performance of the proposed training algorithm, such as convergence rate and stability via extensive numerical evaluations.

Identifying user geolocation in online social networks is an essential task in many locationbased applications.Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior.In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users.This methodology helps with providing meaningful explanations on prediction results.Furthermore, it also initiates an attempt to uncover the so-called "black-box" GNN-based models by investigating the effect of individual nodes.

The surface soil freezing and thawing (F/T) status plays important roles in water and energy exchanges, hydrology cycle and terrestrial ecosystem. Passive microwave remote sensing tends to be one of the most effective ways of monitoring global surface F/T status, due to its strong sensitivity to the changes of liquid water and its short revisiting period. Many surface F/T products have been produced based on the observations from different passive microwave sensors. However, due to the differences in spatial scales, satellite data sources, transit times etc., the accuracy and the consistency of different F/T products need to be well considered in front of the comprehensive utilization. In this study, the surface F/T products with resolutions of 36 km, 25 km, 25 km and 6 km derived from SMAP, SSMIS, AMSR-2, AMSR-2 (downscaling) were validated separately based on the in-situ observations in Ngari prefecture and Maqu on the Qinghai-Tibet Plateau. Then a comparison analysis was conducted on the F/T products derived from SMAP and SSMIS to figure out the consistency between F/T products derived from different sensors. The results show that: 1) the verification accuracy in Maqu is higher than that in Ngari prefecture, which reflects the limited capability of passive microwave in identifying the soil surface F/T status in the arid region. 2) The F/T products with resolutions of 6 km and 25 km, which were both derived from AMSR-2 and have the same transit time, showed similar validation accuracies. 3) There is a good agreement between the F/T products derived from SMAP and that from SSMIS. This study will be conducive to the comprehensive application of multi-scale F/T products in the fields of climate change and ecological environment.