Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Total Environ. 2023 May 15;873:162168. doi: 10.1016/j.scitotenv.2023.162168. Epub 2023 Feb 11.
Lake Taihu, located in a densely populated and highly industrialized area in eastern China, has experienced dramatic changes in water quality since the reform and opening-up in the 1980s. Landsat data can be used to trace water quality changes over approximately 40 years. However, chlorophyll-a (Chla) estimation, which characterizes the trophic status, has not been thoroughly explored (especially in turbid water using wide bandwidth Landsat) due to the interference of suspended particulate matter (SPM) to Chla. In this study, we used Landsat TM/OLI for turbid water Chla inversion and to analyze the spatiotemporal variation of Chla in Lake Taihu for 38 years and its influencing factors. An optical classification algorithm based on R(green)/R(red) was used to exclude highly turbid waters dominated by SPM; Chla was estimated only in waters with low SPM. We constructed an exponential estimation model based on R(NIR)/R(red), and verified the accuracy of the model using the measured Chla synchronized with satellite data. The model was applied to Landsat images to calculate the Chla concentration in Lake Taihu during 1984-2021, and its spatiotemporal distribution patterns were further analyzed. Spatially, the Chla concentrations in the western and northern regions of Lake Taihu were higher than those in other regions, probably because these areas are estuaries with large exogenous pollutant discharge and more nutrients are imported from exogenous sources. Chla showed an overall significant upward trend from 1984 to 2021 probably because of temperature rise, wind speed reduction, and nutrient increase. The results of the spatial and temporal variation of Chla and the influencing factors in this study provide supporting data for eutrophication monitoring and management in Lake Taihu. The proposed Chla estimation method can be extended to assess the spatial and temporal distribution of eutrophication in other inland waters with similar optical properties.
太湖位于中国东部人口密集、高度工业化的地区,自 20 世纪 80 年代改革开放以来,水质发生了巨大变化。陆地卫星数据可用于追踪大约 40 年来的水质变化。然而,由于悬浮颗粒物 (SPM)对 Chla 的干扰,叶绿素-a (Chla)的估算(用于描述营养状态)尚未得到彻底探讨(尤其是在使用宽带宽陆地卫星的混浊水中)。在本研究中,我们使用陆地卫星 TM/OLI 进行混浊水 Chla 反演,并分析了太湖 38 年来 Chla 的时空变化及其影响因素。基于 R(绿色)/R(红色)的光学分类算法用于排除主要由 SPM 控制的高度混浊水域;仅在 SPM 低的水域中估算 Chla。我们构建了基于 R(近红外)/R(红色)的指数估算模型,并使用与卫星数据同步测量的 Chla 对模型的准确性进行验证。该模型应用于陆地卫星图像,以计算 1984-2021 年太湖的 Chla 浓度,并进一步分析其时空分布模式。从空间上看,太湖西部和北部的 Chla 浓度高于其他地区,这可能是因为这些地区是河口,外源污染物排放量较大,外源输入的营养物质较多。1984 年至 2021 年期间,Chla 呈总体显著上升趋势,可能是由于气温升高、风速降低和营养物质增加。本研究中 Chla 的时空变化及其影响因素的结果为太湖富营养化监测和管理提供了支持数据。所提出的 Chla 估算方法可扩展用于评估具有类似光学特性的其他内陆水域的富营养化时空分布。