2024年6月7日，Elsevier 旗下top期刊《Science of The Total Environment》在线发表了云南师范大学地理学部杨昆教授团队的最新研究成果《Analysis of the temporal and spatial evolution of turbidity in Tonle Sap Lake and its influencing factors》。云师大地理学部为第一单位，通讯作者为云南师范大学地理学部杨昆教授。云南师范大学地理学部潘梅娥为共同通讯作者。

https://www.sciencedirect.com/science/article/abs/pii/S0048969724037653

Abstract

Turbidity is a crucial indicator of water quality. The European Commission's Copernicus Land Monitoring Service Platform provides free turbidity data for large lakes to monitor global water quality of lakes. However, data were missing from April 2012 to April 2016, severely limiting the long-term analysis. Based on MODIS and turbidity data, Random Forest and XGBoost models are used to invert Tonle Sap Lake's turbidity. Random Forest outperformed the XGBoost model. Based on Random Forest model, missing data were filled in to construct long-term series data of Tonle Sap Lake turbidity (2004–2021). Trend, persistence and correlation analyses were conducted to reveal spatiotemporal characteristics and driving mechanism of turbidity. The results showed that: (1) spatially, the average annual, monthly, and seasonal turbidity was higher in the north but lower in the south, with regions of higher turbidity exhibiting more significant changes; (2) temporally, the annual turbidity mean was 53.99 NTU and showed an increasing trend. Monthly, turbidity values were higher from March to August and lower from September to February, with the highest and lowest recorded in June and November at 110.06 and 5.82 NTU, respectively. Seasonally, turbidity was higher in spring and summer compared to autumn and winter, with mean turbidity values of 84.16, 93.47, 15.33 and 23.21 NTU, respectively; (3) In terms of sustainability, the Hurst exponent for annual turbidity was 0.23, indicating a reverse trend in the near future; (4) Dam construction's impact on turbidity was not significant. Compared with natural factors (permanent wetlands, grasslands, lake surface water temperature, and remote sensing ecological index), human activities (barren or sparely vegetated, urban and built-up, croplands and population density) had a more significant impact on turbidity. Turbidity was highly correlated with cropland (r = 0.76), followed by population density (r = 0.71), and urban and built-up areas (r = 0.69).

扩展阅读：

杨昆-地理学部

云师大——基础研究之杰出代表杨昆教授

云师大杨昆课题组在Water Research、J HYDROL和ENVIRON POLLUT等高端期刊发文4篇

潘梅娥（1986年—），女，汉族，云南曲靖人，硕士，中共党员。2014年3月入职云南师范大学，讲师，博士研究生。主要承担《遥感原理与方法》、《遥感数字图像处理》、《地理信息科学》等课程的教学工作，其中，《地理信息科学》被评为过程性评价优秀课程。主要的研究方向为环境遥感监测与评价，参与国家自然基金项目3项，发表学术论文10篇，其中，SCI检索3篇，EI检索2篇。