The West Kunlun Mountains (WKL) gather lots of large-scale glaciers, which play an important role in the climate and freshwater resource for central Asia. Despite extensive studies on glaciers in this region, a comprehensive understanding of inter-annual variations in glacier area, flow velocity, and terminus remains lacking. This study used a deep learning model to derive time-series glacier boundaries and the sub-pixel cross-correlation method to calculate inter-annual surface flow velocity in this region from 71 Sentinel-2 images acquired between 2016 and 2024. We analyzed the spatial-temporal variations of glacier area, velocity, and terminus. The results indicate that, as follows: (1) The glacier area in the WKL remained relatively stable, with three glaciers expanding by more than 0.5 km² and five glaciers shrinking by over 0.5 km² from 2016 to 2024. (2) Five glaciers exhibited surging behavior during the study period. (3) Six glaciers, with velocities exceeding 50 m/y, have the potential to surge. (4) There were eight obvious advancing glaciers and nine obvious retreating glaciers during the study period. Our study demonstrates the potential of Sentinel-2 for comprehensively monitoring inter-annual changes in mountain glacier area, velocity, and terminus, as well as identifying glacier surging events in regions beyond the study area.

Satellite-based land surface temperature (LST) products are frequently affected by cloud cover and atmospheric conditions, resulting in missing data that significantly limits the continuous monitoring of the thermal environment in complex terrains, such as the Tibetan Plateau. Existing spatiotemporal interpolation methods face clear accuracy limitations when addressing extensive data gaps, while physical models often struggle due to insufficient meteorological inputs in complex landscapes. Moreover, conventional data-driven approaches usually overlook local spatial variations, resulting in smoothed thermal patterns and systematic errors. To overcome these issues, we propose a Physically Constrained Spatial Residual Learning framework. In this framework, we use the Enhanced Annual Temperature Cycle (EATC) model to capture the temporal baseline of LST first. Then, we integrate multi-source auxiliary data into the Geographical-XGBoost (G-XGBoost) algorithm to model spatial nonlinear residuals. Using simulated cloud masks on the 2017 MODIS LST dataset from the Qinghai–Tibet Engineering Corridor, we show that the hybrid model outperforms both individual physical models and global machine learning models in accuracy and spatial detail recovery. Validation results yield an R² of 0.88, an RMSE of 1.92 K, and a mean bias of 0.07 K. Seasonal evaluations indicate best performance in winter (RMSE = 1.19 K) with robust performance in summer. Furthermore, the framework reduces boundary artifacts and accurately reproduces thermal spatial patterns in complex terrain through adaptive local bandwidth and weight adjustments. This approach provides a reliable method for high-precision LST reconstruction over heterogeneous alpine surfaces.

Single-tree diameter at breast height (DBH) and tree height (H) are fundamental parameters for forest inventory, forest structure characterization, and forest carbon stock estimation. However, single-source LiDAR data cannot simultaneously capture complete trunk and canopy structural information, limiting the accuracy of single-tree structural parameter estimation. To address this issue, a Bi-Directional Cross-Attention Fusion Network (BCAF-Net) is proposed to estimate DBH and H separately by integrating ground-based Simultaneous Localization and Mapping LiDAR (SLAM LiDAR) and Unmanned Aerial Vehicle LiDAR (UAV LiDAR) data. The framework employs a dual-branch encoder and a bidirectional cross-attention mechanism to establish cross-view structural relationships between trunk and canopy observations, enabling effective multi-source feature fusion. Experiments conducted at two urban forest sites demonstrated that BCAF-Net achieved the highest estimation accuracy, with RMSE of 0.82 cm for DBH and 0.91 m for H and corresponding R² values of 0.97 and 0.96, respectively. Furthermore, the model maintained stable performance under varying forest structural complexities, cross-site conditions, and tree species. These results demonstrate that cross-view structural interaction effectively exploits complementary information from SLAM LiDAR and UAV LiDAR data, thereby improving single-tree structural parameter estimation in complex forest environments.

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics.