Microorganisms play a pivotal role in nutrient cycling in aquatic ecosystems. Understanding bacterial diversity and its functional composition are crucial for aquatic ecology and protection. We investigated the bacterial community structure using 16S rRNA gene amplicons high-throughput sequencing in this study. Results showed that 105 amplicon sequence variants (ASVs) account for 43.8% of the total sequences shared by the Nandu River’s lower, middle, and upper reach and the Songtao Reservoir. The dominant bacterial phylum in the Nandu River and its connected Songtao Reservoir were Proteobacteria and Actinobacteriota, respectively. The highest Chao1 and Shannon index values were found in the lower reach of the Nandu River. Beta diversity analysis showed the divergence in bacterial communities in the Nandu River and Songtao Reservoir, but not in different reaches. Among the water properties, based on the Mantel test, dissolved oxygen, total nitrogen, and nitrite significantly affected the bacterial communities. The functional profiles predicted by Tax4Fun2 showed that metabolism was the most abundant function group. The relative abundance of genetic information processing was significantly higher in the Songtao Reservoir than in the Nandu River, while the relative abundance of human diseases was significantly lower in the Songtao Reservoir than in the Nandu River. The appearance of the xenobiotics biodegradation and metabolism function group requires us to pay more attention to possible water pollution, especially at the upper reach of the Nandu River.

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale.

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr⁻¹ (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr⁻¹ (p < 0.05) and a slowed autumn browning rate of −0.6% yr⁻¹ (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone.

Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (L_nir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R² = 0.72, RMSE = 7.52 g/m²), outperforming conventional VIs-based (R² < 0.39, RMSE > 11.13 g/m²) estimations. The study further investigated the results of fAPAR_green-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn < 0.2) and higher diversity conditions (DMn > 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating L_nir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring.