Abstract：
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.
摘要：
呼伦湖面临着严重的水质退化,需要进行有效的安全监测。传统方法缺乏必要的时空覆盖,强调了遥感模型的必要性。在这项研究中,我们利用了Landsat8OLI数据集，综合横断面监测和现场采样数据。采用随机森林算法，我们构建了呼伦湖六个水质参数的遥感反演模型：叶绿素a（Chl-a），总氮(TN)，总磷（TP），氨氮(NH3-N),化学需氧量(COD),和溶解氧（DO）。该模型应用于2016年至2021年呼伦湖非冰期，表现出良好的性能，并生成高分辨率地图。时间序列分析显示，在研究期间，TN的污染水平，TP,呼伦湖的COD非常严重，超过中国地表水环境质量标准的V类水标准。区域分析表明，与湖泊入口相比，中部湖泊地区的污染物浓度较低。高污染河流流入对呼伦湖水质产生不利影响。为确保呼伦湖水质持续健康,必须认真监测湖泊水质，并采取必要措施防止进一步恶化。本研究对制定和实施呼伦湖生态保护与修复策略具有重要意义。
