The Ebinur Lake watershed is an important ecological barrier for environmental changes in the Junggar Basin in Xinjiang Uygur Autonomous Region (XUAR). Due to the tremendous changes in the underlying surface environment of the watershed in the past few decades, the watershed has become a typical region of ecological degradation. Drought affects the surface dynamics and characterizes the regional dry and wet environments, while the dynamic variation in lakes and vegetation are indicators of dynamic changes in land surfaces. Thus, a quantitative assessment of the response of lakes and vegetation to drought conditions at multiple temporal scales is critical for assessing the potential impacts of regional climate change on terrestrial ecosystems and ecological restoration. The standardized precipitation evapotranspiration index (SPEI), the spectral water index (NDWI) and the normalized difference vegetation index (NDVI) were used to analyse the evolution of drought, the variation in lake surface area and the sustainable variation in vegetation. Furthermore, we quantitatively evaluated the response patterns of vegetation to droughts of multiple temporal scales (1-, 3-, 6-, 12-, 24-month). The conclusions showed that (1) overall, the area of Ebinur Lake experienced drastic fluctuations, and the lake area has decreased significantly since 2003, with a dynamic area of 817.63 km² in 2003 and 384.60 km² in 2015, and the lake area had shrank severely. (2) The interannual variation of wet and dry changed alternately during the observation period, and persistent drought events occurred from 2006 to 2010 across the Ebinur Lake watershed. (3) The vegetation area of cultivated land expanded continuously across the watershed, and the grassland degraded severely. (4) The changes in lake surface area are significantly correlated with the inflow water volume (correlation coefficient = 0.64, P < 0.01). (5) The vegetation of different terrestrial ecosystems exhibited heterogeneous responses to multiple temporal scales of drought in different seasons. The percentage was 72.78% of the total area, which showed a correlation between vegetation and drought conditions during the growing season period, and there were more impacts of drought on vegetation, with values as high as 64.33% of the area in summer, than those in other seasons.

In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation.

Drought disasters significantly threaten the stability of terrestrial ecosystems in arid and semi-arid regions. Although the impact of drought on vegetation has been extensively researched, there is still a lack of understanding regarding the ecological impacts of drought on the heterogeneous vegetation in northern China’s arid and semi-arid regions. To investigate the spatial patterns of vegetation and differences in response to drought climate, the Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to determine vegetation characteristics and the seasonal sensitivity of vegetation types to drought response, as well as to identify the factors driving the sensitivity differences. In the study area, 83.1% of the vegetation exhibited a significant positive correlation with the drought index. The maximum correlation coefficient between vegetation and drought was 0.62, demonstrating that the majority of vegetation was influenced by drought. It was observed that different vegetation types responded differently to drought at varying spatial and temporal scales. The main drivers of vegetation response to drought in arid and semi-arid zones were quantified by using the random forest algorithm. The results indicated that climate and soil factors are the primary limiting factors. These findings enhance our comprehension of arid and semi-arid areas, the effect of drought on different plant species, and the factors that affect vegetation’s response to drought.

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture.

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions.

Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSI_O) index exhibited the best monitoring performance, with a field determination coefficient R² of 0.5713 (experimental field R² = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring.