Opt Express. 2022 Mar 28;30(7):10329-10345. doi: 10.1364/OE.453404.
The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07-0.2 mg L), Secchi disk depth (SDD, 0.1-0.78 m), and chlorophyll a (Chla, 0.11-20.41 μg L). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.
营养状态指数（TSI）是水生生态系统评估的一个重要参数。由于富营养化过程的多变量复杂性，通过遥感估算 TSI 仍然具有挑战性。本研究于 2020 年 10 月收集了中国东北 7 个湖泊的综合现场光谱-生物地球化学数据集。该数据集涵盖了从贫营养到富营养的营养状态，总磷（TP，0.07-0.2mg/L）、透明度（SDD，0.1-0.78m）和叶绿素 a（Chla，0.11-20.41μg/L）范围广泛。在这里，我们提出了一种从遥感数据估算 TSI 的经验方法。首先，通过波段比/波段组合模型估算 TP、SDD 和 Chla。然后，使用 Carlson 模型估算 TSI，该模型具有较高的 R（0.88）、较低的 RMSE（3.87）和较低的 MRE（6.83%）。TP、SDD 和 Chla 的协同作用主导了营养状态，改变了水柱中的光分布，影响了光谱特征。此外，从三元图来看，各参数对富营养化的贡献在研究湖泊之间有所不同。高 Chla 浓度是 HMT 湖富营养化的主要原因，占比 45.4%，高于其他两个参数。然而，在 XXK 湖，高 TP 浓度是富营养化的主要原因，占比 66.8%，而不是 Chla 和 SDD。总体而言，TP 主导了营养状态，SDD 占所有采样湖泊贡献的 85.6%。此外，我们发现使用单参数指数评估湖泊营养状态会导致很大的偏差，甚至存在两个级别的差异。因此，强烈建议使用多参数 TSI 来评估湖泊的营养状态。总之，本研究结果为未来利用卫星图像数据估算湖泊 TSI 提供了理论和方法基础，有助于水质监测和管理。
