This study uses the Kherlen River as a case study to investigate the relationship between soil nutrients in riparian zones and water quality in inlet sections of lakes. Field sampling and experimental analyses were conducted during the high-water period (July) of 2023. An investigation was conducted on both the water quality of the river segments entering the lake and the soil nutrients. Methods such as the comprehensive water quality index (WQI), spatial heterogeneity analysis, and gray relational analysis were employed to assess water quality, soil nutrient characteristics, and their interrelationships, respectively. The results indicated that during the high-water period, the average concentrations of the permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), and dichromate oxidizability (COD_Cr) in the Kherlen River exceeded the Class V surface water quality standard thresholds. The overall WQI of the Kherlen River was 22.54, reflecting generally poor water quality, with a Global Moran’s I of 0.21, indicating a spatially clustered distribution. In the watershed, the Global Moran’s I values for pH values, TOC, TN, and TP at soil depths of 0–10 cm and 10–20 cm were 0.52, 0.90, 0.86, and 0.94 and 0.51, 0.57, 0.77, and 0.78, respectively. A significant positive correlation was found among soil nutrients, exhibiting a strong spatial aggregation characteristic, with nutrient concentrations decreasing with increasing soil depth. Moreover, the WQI of the Kherlen River demonstrated a significant correlation (R² > 0.6) with soil nutrient indicators, underscoring the substantial impact of riparian soil nutrients on river water quality. Based on these findings, targeted water management and ecological restoration measures are proposed to improve the water quality of the Kherlen River and Hulun Lake, providing new insights and scientific evidence for the restoration and sustainable development of lake ecosystems.

Exploring eco-environmental quality dynamics in the Daihai Lake Basin has significant implications for the conservation of ecological environments in the semi-arid and arid regions of northern China. Based on the Google Earth Engine (GEE) platform, the remote sensing ecological index (RSEI) was constructed by coupling Landsat SR remote sensing data from 1985 to 2022. The spatial significance of the RSEI was analyzed using linear regression equations and an F-test. The spatial correlation, distribution characteristics, and driving factors behind the RSEI were explored using Moran’s index and a geodetector. The results indicated that (1) the RSEI was appropriate for evaluating eco-environmental quality in the Daihai Lake Basin. (2) From 1985 to 2022, the eco-environmental quality of the Daihai Lake Basin exhibited a positive trend but remained subpar. (3) A positive spatial autocorrelation was demonstrated for eco-environmental quality with increasing spatial aggregation. (4) Significant eco-environmental quality degradation (slope < 0) occurred primarily in Sanyiquan Town in the northeastern region of the basin and in Tiancheng Township in the southeastern region. Conversely, a notable improvement (slope > 0) was predominantly observed in Yongxing and Liusumu in southwestern Daihai. (5) The improvement in the ecological environment of the Daihai Lake Basin was primarily attributed to an increase in NDVI and WET and a decrease in NDBSI and LST. The interaction between NDVI and LST had the greatest explanatory power for the ecological environment. Among the external driving factors, DEM (elevation) was the dominant factor in the RSEI and had the strongest explanatory power. The interaction between DEM and LST was the most significant, and the driving factors were enhanced. This study provided a theoretical basis for the sustainable development of the Daihai Lake Basin, which is crucial for the local ecological environment and economic development.