Lu Shijiao, Bian Yingchun, Chen Fangfang, Lin Jie, Lyu Heng, Li Yunmei, Liu Huaiqing, Zhao Yang, Zheng Yiling, Lyu Linze
Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, PR China.
Co-Innovation Center for Sustainable Forestry in Southern China of Jiangsu Province, Key Laboratory of Soil and Water Conservation and Ecological Restoration of Jiangsu Province, Nanjing Forestry University, Nanjing, 210037, PR China.
Environ Res. 2023 Nov 15;237(Pt 1):116898. doi: 10.1016/j.envres.2023.116898. Epub 2023 Aug 15.
Water clarity is a critical parameter of water, it is typically measured using the setter disc depth (SDD). The accurate estimation of SDD for optically varying waters using remote sensing remains challenging. In this study, a water classification algorithm based on the Landsat 5 TM/Landsat 8 OLI satellite was used to distinguish different water types, in which the waters were divided into two types by using the a(443)/a(443) ratio. Water type 1 refers to waters dominated by phytoplankton, while water type 2 refers to waters dominated by non-algal particles. For the different water types, a specific algorithm was developed based on 994 in situ water samples collected from Chinese inland lakes during 42 cruises. First, the R(443)/R(655) ratio was used for water type 1 SDD estimation, and the band combination of (R(443)/R(655) - R(443)/R(560)) was proposed for water type 2. The accuracy assessment based on an independent validation dataset proved that the proposed algorithm performed well, with an R of 0.85, mean absolute percentage error (MAPE) of 25.98%, and root mean square error (RMSE) of 0.23 m. To demonstrate the applicability of the algorithm, it was extensively evaluated using data collected from Lake Erie and Lake Huron, and the estimation accuracy remained satisfactory (R = 0.87, MAPE = 28.04%, RMSE = 0.76 m). Furthermore, compared with existing empirical and semi-analytical SDD estimation algorithms, the algorithm proposed in this paper showed the best performance, and could be applied to other satellite sensors with similar band settings. Finally, this algorithm was successfully applied to map SDD levels of 107 lakes and reservoirs located in the Middle-Lower Yangtze Plain (MLYP) from 1984 to 2020 at a 30 m spatial resolution, and it was found that 53.27% of the lakes and reservoirs in the MLYP generally show an upward trend in SDD. This research provides a new technological approach for water environment monitoring in regional and even global lakes, and offers a scientific reference for water environment management of lakes in the MLYP.
水体透明度是水质的一个关键参数，通常使用沉降盘深度（SDD）来测量。利用遥感技术准确估算光学性质变化水体的SDD仍然具有挑战性。在本研究中，基于陆地卫星5号专题制图仪（TM）/陆地卫星8号业务陆地成像仪（OLI）卫星的水分类算法被用于区分不同的水体类型，其中利用a(443)/a(443)比值将水体分为两类。1型水体是指以浮游植物为主的水体，而2型水体是指以非藻类颗粒为主的水体。针对不同的水体类型，基于42个航次从中国内陆湖泊采集的994个现场水样，开发了一种特定算法。首先，利用R(443)/R(655)比值估算1型水体的SDD，并针对2型水体提出了(R(443)/R(655) - R(443)/R(560))波段组合。基于独立验证数据集的精度评估证明，所提出的算法性能良好，决定系数R为0.85，平均绝对百分比误差（MAPE）为25.98%，均方根误差（RMSE）为0.23米。为了证明该算法的适用性，利用从伊利湖和休伦湖收集的数据进行了广泛评估，估算精度仍然令人满意（R = 0.87，MAPE = 28.04%，RMSE = 0.76米）。此外，与现有的经验和半分析SDD估算算法相比，本文提出的算法表现出最佳性能，并且可以应用于具有类似波段设置的其他卫星传感器。最后，该算法成功应用于绘制1984年至2020年位于长江中下游平原（MLYP）的107个湖泊和水库的SDD水平图，空间分辨率为30米，结果发现MLYP中53.27%的湖泊和水库的SDD总体呈上升趋势。本研究为区域乃至全球湖泊的水环境监测提供了一种新的技术方法，并为MLYP湖泊的水环境管理提供了科学参考。
