The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p < 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.

Water use efficiency (WUE) has garnered considerable attention at global and regional levels. However, spatio-temporal variations of WUE and related influencing factors in the complex karst landforms of southwest China require further elucidation. Herein, the ratio of gross primary productivity (GPP) to evapotranspiration (ET) obtained through the PML-V2 product was used to characterize ecosystem WUE, the spatio-temporal variations to ecosystem WUE, and responses to temperature, precipitation, and the enhanced vegetation index (EVI) in southwest China. The results showed that: (1) The ecosystem WUE in southwest China decreased with increasing latitude and altitude. Spatially, the ecosystem WUE fluctuates in a “W” pattern with increasing longitude because of the karst landforms’ distribution patterns. (2) The non-significant trend in increased ecosystem WUE during 2003–2017 may be associated with significant increases in the ET offsetting part of the GPP contribution to ecosystem WUE. Spatial distribution of changes in WUE is similar to GPP owing to the dominant role of GPP in changes to ecosystem WUE. (3) The multi-year average ecosystem WUE was lower in karst than in non-karst landforms; however, vegetation restoration projects have contributed in significantly increasing variation rate of ecosystem WUE in karst than that in non-karst landforms. (4) Temperature, precipitation, and EVI were generally positively correlated with ecosystem WUE and were important factors for the increase in ecosystem WUE. EVI characterized vegetation restoration indicators showed that the ecological engineering construction in the study area was effective and was the dominant factor of ecosystem WUE change in 59.59% of the study area. The results of this study are important for further understanding carbon and water cycling processes in karst regions.

Accurate and automated identification of water bodies from satellite imagery is critical for environmental monitoring, water resource management, and disaster response. Current deep learning approaches, however, suffer from a strong dependence on manual hyperparameter tuning, which limits their automation capability and robustness in complex, multi-scale scenarios. To overcome this limitation, this study proposes a fully automated segmentation framework that synergistically integrates an enhanced U-Net model with a novel hybrid evolutionary optimization strategy. Extensive experiments on public Kaggle and Sentinel-2 datasets demonstrate the superior performance of our method, which achieves a Pixel Accuracy of 96.79% and an F1-Score of 94.75, outperforming various mainstream baseline models by over 10% in key metrics. The framework effectively addresses the class imbalance problem and enhances feature representation without human intervention. This work provides a viable and efficient path toward fully automated remote sensing image analysis, with significant potential for application in large-scale water resource monitoring, dynamic environmental assessment, and emergency disaster management.