Previous studies have shown the challenges in using a single model to estimate chlorophyll-a concentration (Chl-a) in water bodies with widely differing characteristics. A single model based on remote sensing to map the Chl-a distribution across the entire Tietê River Cascade System (TRCS) serves as a cost and time-efficient alternative to the conventional monitoring by providing trophic status over space and time. The Tietê River contains one of the largest cascade reservoir systems in the world, which sustains important ecological and socio-economic activities in the São Paulo State, Brazil. Surplus nutrients in water draining its surrounding catchments have been the main cause of eutrophication in the reservoirs of the TRCS. To assess the trophic state of the reservoirs, Chl-a has been regularly monitored by sampling points. However, they are limited by operational costs and dependent on weather conditions. Moreover, the current sampling method only produces point-based measurements. In this paper, we calibrate remote sensing images based on the absorption coefficient to map the spatial distribution patterns of Chl-a levels in the reservoirs. Mapping is done by estimating the Chl-a concentration. The absorption coefficients were retrieved from OLI/Landsat images using the Quasi-Analytical Algorithm (QAA). The total absorption (a_t) in 482 nm and 655 nm retrieved by QAA presented NRMSE of 17% and 18.5%, respectively. Both a_t (482 and 655 nm) were used in the model calibration and presented a satisfactory result covering all data ranges, with R² of 0.646 and NRMSE of 15.3%. The proposed model in this study to retrieve Chl-a maps with relatively high accuracy can be incorporated into the operational monitoring system of the TRCS at a low cost that can provide timely information for reservoir managers to carry out necessary actions. This may include mitigating environmental impacts caused by sudden algae blooms.

先前的研究表明，使用单一模型来估计具有广泛不同特征的水体中的叶绿素a（Chl- a）浓度是一项挑战。基于遥感的单个模型来映射Chl-一个通过提供整个空间和时间的营养状态，整个Tietê河小瀑布系统（TRCS）的分布可作为传统监测的一种既省钱又省时的选择。Tietê河是世界上最大的梯级水库系统之一，维持着巴西圣保罗州的重要生态和社会经济活动。排水至周围集水区的水中过多的养分一直是TRCS水库富营养化的主要原因。为了评估该水库的营养状态，Chl-一已由采样点定期监控。但是，它们受到运营成本的限制，并且取决于天气状况。而且，当前的采样方法仅产生基于点的测量。在本文中，我们根据吸收系数校准遥感图像，以绘制储层中Chl- a水平的空间分布模式。映射由估计Chl-完成一个浓度。使用准分析算法（QAA）从OLI / Landsat图像中检索吸收系数。通过QAA回收的482 nm和655 nm的总吸收（a _t）分别显示出17％和18.5％的NRMSE。既是_Ť（482和655 nm）（482和655 nm）用于模型校准，并给出了覆盖所有数据范围的令人满意的结果，R ²为0.646，NRMSE为15.3％。本研究中提出的用于以相对较高的精度检索Chl- a图的模型可以以较低的成本并入TRCS的运行监控系统中，从而可以为油藏管理者提供及时的信息以执行必要的操作。这可能包括减轻藻类突然开花对环境的影响。