Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resource, Nanjing 210023, China.
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Water Res. 2022 May 15;215:118213. doi: 10.1016/j.watres.2022.118213. Epub 2022 Feb 26.
Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, the estimation of total phosphorus (TP) concentration in eutrophic water using remote sensing technology is of great significance for lake environmental management. However, there is no TP remote sensing model for lake groups, and thus far, specific models have been used for specific lakes. To address this issue, this study proposes a framework for TP estimation. First, three algorithm development frameworks were compared and applied to the development of an algorithm for Lake Taihu, which has complex water environment characteristics and is a representative of eutrophic lakes. An Extremely Gradient Boosting (BST) machine learning framework was proposed for developing the Taihu TP algorithm. The machine learning algorithm could mine the relationship between FAI and TP in Lake Taihu, where the optical properties of the water body are dominated by phytoplankton. The algorithm exhibited robust performance with an R value of 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%). Then, a general TP algorithm (R = 0.64, RMSE = 0.06 mg/L, MRE = 34.13%) was developed using the proposed framework and tested in seven other lakes using synchronous image data. The algorithm accuracy was found to be affected by aquatic vegetation and enclosure aquaculture. Third, compared with field investigations in other studies on Lake Taihu, the Taihu TP algorithm showed good performance for long-term TP estimation. Therefore, the machine learning framework developed in this study has application potential in large-scale spatio-temporal TP estimation in eutrophic lakes.
磷是淡水生态系统中的限制营养物质。因此，利用遥感技术估算富营养化水体中的总磷（TP）浓度对于湖泊环境管理具有重要意义。然而，目前还没有针对湖泊群的 TP 遥感模型，迄今为止，都是针对特定湖泊使用特定模型。针对这一问题，本研究提出了一种 TP 估算框架。首先，比较了三种算法开发框架，并将其应用于开发具有复杂水环境特征且是富营养化湖泊代表的太湖 TP 算法。提出了一种极端梯度提升（BST）机器学习框架来开发太湖 TP 算法。该机器学习算法可以挖掘太湖水体中 FAI 与 TP 之间的关系，其中水体的光学特性主要由浮游植物决定。该算法在太湖中表现出稳健的性能，R 值为 0.6（RMSE = 0.07 mg/L，MRE = 43.33%）。然后，使用所提出的框架开发了一种通用的 TP 算法（R = 0.64，RMSE = 0.06 mg/L，MRE = 34.13%），并使用同步图像数据在其他七个湖泊中进行了测试。发现算法的准确性受到水生植被和围网养殖的影响。第三，与其他关于太湖的研究中的实地调查相比，太湖 TP 算法在长期 TP 估算方面表现出良好的性能。因此，本研究中开发的机器学习框架在富营养化湖泊的大规模时空 TP 估算中具有应用潜力。
