Water-use efficiency (WUE) is not only an important indicator to connect the carbon and water cycles of a terrestrial ecosystem, but also a key parameter for an ecosystem to respond to climate change. It is crucial for understanding the mechanism of regional ecosystem response to environmental change by researching the influences of vegetation and climate change on WUE variation and revealing its drivers. Based on trend analysis, grey relational analysis, and ridge-regression analysis, this study analyzed the spatiotemporal variation characteristics of WUE in Inner Mongolia (IM) from 2001 to 2018 and determined the dominant influencing factors of WUE variation. The results showed that the annual mean WUE in IM was 1.39 g C m⁻² mm⁻¹ and it generally presented a rising trend, with an increasing rate of 0.0071 g C m⁻² mm⁻¹ yr⁻¹. Leaf-area index (LAI) and precipitation were the most important factors influencing WUE in IM, followed by relative humidity and wind speed. Temperature, water vapor pressure and sunshine duration slightly influenced WUE and they were relatively less important. According to the ridge-regression analysis, LAI, precipitation and relative humidity had a positive contribution to WUE variation, while the wind speed had a negative contribution. Regionally, LAI was the dominant cause of WUE variation. The contribution and relative contribution rate of LAI to WUE variation were 0.008 g C m⁻² mm⁻¹ yr⁻¹ and 44.57%, which were significantly higher than those of precipitation, relative humidity, and sunshine duration. Thus, vegetation primarily dominated WUE variability during the study period. The relative contribution rate of LAI varied across the different vegetation types and ranged from 25.26% in swamps to 51.29% in meadows. Our results improve the understanding of the effects of driving factors on WUE, which can help policymakers with water resource management and ecological restoration.

Mapping glacial lakes is a prerequisite for understanding their responses to climate changes and assessing potential danger of glacial lake outburst floods (GLOFs). Although remote sensing technology has enabled continuous monitoring and assessment of global glacial lake evolution, accurately and reliably extracting glacial lakes in high mountain areas remains challenging. This study proposed a glacial lake mapping framework based on multisource remote sensing technique and an improved deep learning (DL) model to address diverse challenges associated with glacial lake mapping in high mountain areas. Test results obtained in the Southeast Tibetan Plateau (TP) region demonstrate that the framework achieves high accuracy, with measures of Dice, precision, recall, and intersection over union (IOU) reaching 0.8986, 0.9009, 0.8963, and 0.8287, respectively. It effectively mitigates the impacts of cloud cover, shadowing, glacial debris, lake-water turbidity, and freeze-thaw lake-water conditions on glacial lake delineation. This study provided a concrete solution for glacial lake mapping in high mountain areas with complex topography, and it supported technical advancements in GLOF risk identification.