Shang Yingxin, Song Kaishan, Lai Fengfa, Lyu Lili, Liu Ge, Fang Chong, Hou Junbin, Qiang Sining, Yu Xiangfei, Wen Zhidan
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng 252000, China.
Water Res. 2023 Feb 15;230:119540. doi: 10.1016/j.watres.2022.119540. Epub 2022 Dec 29.
The pollution or eutrophication affected by dissolved organic matter (DOM) composition and sources of inland waters had attracted concerns from the public and government in China. Combined with remote sensing techniques, the fluorescent DOM (FDOM) parameters accounted for the important part of optical constituent as chromophoric dissolved organic matter (CDOM) was a useful tool to trace relative DOM sources and assess the trophic states for large-scale regions comprehensively and timely. Here, the objective of this research is to calibrate and validate a general model based on Landsat 8 OLI product embedded in Google Earth Engine (GEE) for deriving humification index (HIX) based on EEMs in lakes across China. The Landsat surface reflectance was matched with 1150 pairs fieldtrip samples and the nine sensitive spectral variables with good correlation with HIX were selected as the inputs in machine learning methods. The calibration of XGBoost model (R = 0.86, RMSE = 0.29) outperformed other models. Our results indicated that the entire dataset of HIX has a strong association with Landsat reflectance, yielding low root mean square error between measured and predicted HIX (R = 0.81, RMSE = 0.42) for lakes in China. Finally, the optimal XGBoost model was used to calculate the spatial distribution of HIX of 2015 and 2020 in typical lakes selected from the Report on the State of the Ecology and Environment in China. The significant decreasing of HIX from 2015 to 2020 with trophic states showed positive control of humification level of lakes based on the published document of Action plan for prevention and control of water pollution in 2015 of China. The calibrated model would greatly facilitate FDOM monitoring in lakes, and provide indicators for relative DOM sources to evaluate the impact of water protection measures or human disturbance effect from Covid-19 lockdown, and offer the government supervision to improve the water quality management for lake ecosystems.
内陆水体中溶解性有机物（DOM）的组成和来源所导致的污染或富营养化问题，已引起中国公众和政府的关注。结合遥感技术，荧光DOM（FDOM）参数作为发色溶解性有机物（CDOM）是光学成分的重要组成部分，是一种全面、及时地追踪相对DOM来源和评估大尺度区域营养状态的有用工具。在此，本研究的目的是校准和验证一个基于谷歌地球引擎（GEE）中Landsat 8 OLI产品的通用模型，用于推导中国湖泊中基于激发发射矩阵（EEMs）的腐殖化指数（HIX）。将Landsat地表反射率与1150对实地采样样本进行匹配，并选择与HIX具有良好相关性的9个敏感光谱变量作为机器学习方法的输入。XGBoost模型的校准（R = 0.86，RMSE = 0.29）优于其他模型。我们的结果表明，HIX的整个数据集与Landsat反射率有很强的相关性，中国湖泊实测和预测的HIX之间的均方根误差较低（R = 0.81，RMSE = 0.42）。最后，使用最优的XGBoost模型计算了从《中国生态环境状况报告》中选取的典型湖泊2015年和2020年HIX的空间分布。根据中国2015年《水污染防治行动计划》的发布文件，2015年至2020年HIX随营养状态的显著下降表明湖泊腐殖化水平得到了有效控制。校准后的模型将极大地促进湖泊中FDOM的监测，并为评估水污染防治措施或新冠疫情封锁造成的人为干扰影响提供相对DOM来源的指标，为政府监督提供依据，以改善湖泊生态系统的水质管理。
