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As a region susceptible to the impacts of climate change, evaluating the temporal and spatial variations in ecological environment quality (EEQ) and potential influencing factors is crucial for ensuring the ecological security of the Tibetan Plateau. This study utilized the Google Earth Engine (GEE) platform to construct a Remote Sensing-based Ecological Index (RSEI) and examined the temporal and spatial dynamics of the Tibetan Plateau's EEQ from 2000 to 2022. The findings revealed that the RSEI of the Tibetan Plateau predominantly exhibited a slight degradation trend from 2000 to 2022, with a multi-year average of 0.404. Utilizing SHAP (Shapley Additive Explanation) to interpret XGBoost (eXtreme Gradient Boosting), the study identified that natural factors as the primary influencers on the RSEI of the Tibetan Plateau, with temperature, soil moisture, and precipitation variables exhibiting higher SHAP values, indicating their substantial contributions. The interaction between temperature and precipitation showed a positive effect on RSEI, with the SHAP interaction value increasing with rising precipitation. The methodology and results of this study could provide insights for a comprehensive understanding and monitoring of the dynamic evolution of EEQ on the Tibetan Plateau amidst the context of climate change. [ABSTRACT FROM AUTHOR]

As a climate-sensitive and fragile region, how the Tibetan Plateau (TP) responds to global warming would have significant implications for its ecological environment and sustainable socio-economic development. A crucial tool for assessing fine-scale climate impacts is the NEX-GDDP-CMIP6 dataset. This study utilized the NEX-GDDP-CMIP6 dataset to determine changes in extreme temperature and precipitation indices under different Shared Socioeconomic Pathways (SSP) relative to the baseline warming by 1.5 °C, 2 °C, and 3 °C. By using the Theil-Sen median trend and Mann-Kendall test methods, the research concluded that warming trends and increased humidity were on an upward trend. These changes led to amplified fluctuations in the frequency, intensity, and duration of multiple extreme indices. [ABSTRACT FROM AUTHOR]

Water quality research relies on field sampling, which is often very difficult to obtain, especially in arid areas. This study chose the Ebinur Lake Watershed in arid region as a study area. It analyzed 12 water quality parameters (WQPs) and hyper-spectral derived from 48 field samples. Parallel factor analysis (PARAFAC) method was employed to extract four fluorescent components from the fluorescence excitation-emission matrix (EEM) data. Four fluorescence spectral indices (Fn (355), the fluorescence index (FI), the humification index (HIX)) were used to characterize the organic matter. Estimated WQPs were then coupled with hyper-spectral and three-dimensional fluorescence technologies using the Back Propagation-Artificial Neural Network (BPANN) method developed in this study. The main findings are: (1) Higher correlations exist among the reflectance peaks, spectral indices (DI, NDI, RI) and WQPs, which is helpful to improve the accuracy of water quality estimation. (2) There are also high correlations among fluorescent components (peak) (C1, W2, W3, W4, W5 and W7), fluorescence spectral index and some of WQPs. This indicated that fluorescent components and fluorescence indices can be used to accurately monitor WQPs in surface water. (3) The BPANN model has a great potential for estimating WQPs, because of residual predictive deviation (RPD) of estimation model and verify model more than 1.4. These preliminary results have proved that hyper-spectral and fluorescence technologies is a valuable tool for monitoring surface water quality. • Identify water reflectance peaks from hyper-spectral (HP) data collected from field. • Extract HP sensitive bands and spectral indices for water quality (WQ) estimation. • Derive fluorescent components and determine fluorescence spectral index. • Develop a new model based on Grey Relational Analysis-BPANN to assess WQ. [ABSTRACT FROM AUTHOR]