School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China.
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
Environ Pollut. 2022 Mar 1;296:118740. doi: 10.1016/j.envpol.2021.118740. Epub 2021 Dec 28.
Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (C) and its regulatory factors is essential to improving our understanding of its impact on inland water eutrophication, but few studies have assessed this in eutrophic inland lakes due to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. We developed a novel semi-analytical algorithm for this purpose and tested it in the eutrophic Lake Taihu, China. Our algorithm produced robust results with a mean absolute square percentage error of 29.65% and root mean square error of 9.54 μg/L. Meanwhile, the new algorithm demonstrates good portability to other waters with different optical properties and could be applied to various image data, including Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Color Instrument (OLCI). Further analysis based on Geostationary Ocean Color Imager observations from 2011 to 2020 revealed a significant spatiotemporal heterogeneity of C in Lake Taihu. Correlation analysis of the long-term trend between C and driving factors demonstrated that air temperature is the dominant regulating factor in variations of C. This study provides a novel algorithm allowing remote-sensing monitoring of C in eutrophic lakes and can lead to new insights into the role of dissolved phosphorus in water eutrophication.
理解总溶解磷浓度（C）的时空动态及其调节因子对于提高我们对内陆水富营养化影响的认识至关重要，但由于缺乏允许使用遥感数据的合适的生物光学算法，因此很少有研究评估富营养化内陆湖泊中的这一点。我们为此开发了一种新的半分析算法，并在中国富营养化的太湖进行了测试。我们的算法产生了稳健的结果，平均绝对平方百分比误差为 29.65%，均方根误差为 9.54μg/L。同时，新算法对具有不同光学特性的其他水域具有良好的可移植性，可以应用于各种图像数据，包括中分辨率成像光谱仪（MERIS）、中等分辨率成像光谱仪（MODIS）和海洋陆地颜色仪器（OLCI）。基于 2011 年至 2020 年地球静止海洋成像仪观测的进一步分析显示，太湖 C 具有显著的时空异质性。C 与驱动因素之间的长期趋势的相关分析表明，气温是 C 变化的主要调节因素。本研究提供了一种允许对富营养化湖泊中 C 进行遥感监测的新算法，并可以深入了解溶解磷在水体富营养化中的作用。
