Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing, 210023, People's Republic of 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, People's Republic of China.
Environ Sci Pollut Res Int. 2023 May;30(23):64203-64220. doi: 10.1007/s11356-023-26876-8. Epub 2023 Apr 15.
Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.
颗粒态有机物（POM）在淡水生态系统中起着重要作用，是各种养分转化的桥梁。内陆湖泊中 POM 的组成和来源复杂，通过遥感准确估计其浓度较为困难。因此，提出了一种基于 POM 来源和组成的分类方法来估计内陆湖泊中的 POM 浓度。本研究采集了长江三角洲（YRD）10 个湖泊不同时间的 379 个样本。基于 OLCI[公式：见文本]、[公式：见文本]、[公式：见文本]和[公式：见文本]，开发了一种基于生物光学特性的水型分类方法来估算 POM。水型 1 较为清澈，POM 可能来自水生植被或沉积物。水型 2 以无机悬浮物为主，POM 主要来自无机矿物质的附着和夹带。水型 3 是藻类占主导地位的水体，POM 主要来自新鲜藻类颗粒和浮游植物的微生物降解。因此，为每种水型开发了特定的 POM 估算算法。OLCI[公式：见文本]、[公式：见文本]、[公式：见文本]和[公式：见文本]用于水型 1；[公式：见文本]、[公式：见文本]和[公式：见文本]用于水型 2；[公式：见文本]、[公式：见文本]和[公式：见文本]用于水型 3。使用独立数据集评估所开发算法的估计精度，结果表明，与使用的另外两种算法相比，该算法的估计性能显著提高；平均绝对百分比误差（MAPE）从 72.56%和 52.21%分别降低到 32.61%，均方根误差（RMSE）从 3.05mg/L 和 2.24mg/L 分别降低到 1.75mg/L。大气校正的随机误差分析表明，该算法具有鲁棒性，在 30%的随机误差范围内仍能很好地工作。最后，该方法成功应用于 2020 年 11 月 12 日 OLCI 图像估算长江三角洲的 POM 浓度。
