Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon.
Environ Monit Assess. 2023 Jul 6;195(8):923. doi: 10.1007/s10661-023-11456-7.
Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.
人为富营养化是一个全球性的环境问题，威胁着许多内陆淡水的生态功能，降低了它们满足指定用途的能力。世界各地的水务当局都在努力提高监测、预测和管理有害藻类大量繁殖（HABs）事件的能力。虽然大多数水质管理决策仍然基于缺乏有效湖泊/水库管理所需时空分辨率的传统监测方案，但遥感的最新进展为更好地了解这些重要淡水系统的水质变异性提供了新的机会。本研究评估了使用 Sentinel 2 多光谱仪器来预测和评估 Qaraoun 水库水质时空变化的潜力，Qaraoun 水库是一个监测不善的地中海超富营养单型水库，经常发生大规模的 HABs。这项工作首先评估了在使用 Sentinel 2 数据时，转移和重新校准以前为特定水库开发的 Landsat 7 和 8 水质模型的能力。结果表明，Landsat 和 Sentinel 2 之间的可转移性较差，大多数模型即使经过重新校准，其预测能力也会显著下降。然后，根据两年内收集的 153 个水质样本，为水库开发了 Sentinel 2 模型。模型探索了不同的函数形式，包括多元线性回归（MLR）、多元自适应回归样条（MARS）、随机森林（RF）和支持向量回归（SVR）。结果表明，RF 模型在预测叶绿素-a、总悬浮固体、塞奇盘深度和藻蓝蛋白方面优于 MLR、MARS 和 SVR 模型。RF 模型的决定系数（R）从 TSS 的 85%到 SDD 的 95%不等。此外，该研究还探讨了通过受益于蓝藻毒素水平与叶绿素-a 浓度之间的强关系，从 Sentinel 2 MSI 图像中间接量化蓝藻毒素浓度的潜力。
