Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH₃−N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

呼伦湖面临着严重的水质恶化，需要有效的安全监测。传统方法缺乏必要的空间和时间覆盖范围，这凸显了对遥感模型的需求。在本研究中，我们利用了Landsat 8 OLI数据集，综合结合了断面监测和现场采样数据。采用随机森林算法，构建了呼伦湖叶绿素a（Chl-a）、总氮（TN）、总磷（TP）、氨氮（NH ₃ −N)、化学需氧量 (COD) 和溶解氧 (DO)。该模型应用于2016年至2021年呼伦湖不结冰期，表现出值得称赞的性能并生成了高分辨率地图。时间序列分析显示，研究期内呼伦湖TN、TP、COD污染程度极为严重，超过我国地表水环境质量标准Ⅴ类水标准。区域分析表明，与湖入口相比，中心湖区的污染物浓度较低。入湖河流污染严重，对呼伦湖水质造成不利影响。为确保呼伦湖水质持续健康，必须密切监测湖水水质，并采取必要措施，防止进一步恶化。这项研究对于呼伦湖生态保护和恢复战略的制定和实施具有至关重要的意义。