Water quality monitoring plays an essential role in water resource management and water governance. At present, the monitoring is commonly conducted via in-situ sampling and/or by setting up gauging stations, which can be labour intensive and costly. Recently, the possibility of monitoring water quality through remote sensing with Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors has shown great promise, with the key advantages of larger spatial coverage and possibly higher accuracy enabled by higher spectral resolution and more extensive data. Correspondingly, more advanced methods need to be established for hyperspectral analysis for water quality determination to capitalize on this wealth of information. In this study, a new method called Hierarchical Bayesian Model Aggregation for Optimal Multiple Band Ratio Analysis (HBMA-OMBRA) has been developed as a proof-of-concept for estimating Total Suspended Solids (TSS) concentrations from the hyperspectral data. The method leverages on the Bayesian ensembling of competing models because there is not a single best working model for all situations. It also encompasses a new approach called Ensemble Band Ratio Selection (ENBRAS) for the identification of best candidate band ratios (BBRs) via a set of ensembling and “bagging” procedures, followed by a modified Batchelor Wilkin’s algorithm to cluster the candidate band ratios. A laboratory investigation was conducted in the present study to measure the hyperspectral reflectance in different experiments under various environmental conditions to verify the robustness of HBMA-OMBRA. From the experimental results, six distinct clusters of candidate BBRs were identified using ENBRAS. In particular, two clusters in the red, green, and near infrared spectrum showed the largest contribution. The significance of multi-clusters provides an explanation for previously contrasting results reported in the literature and some evidence for reconciling these findings.

水质监测在水资源管理和水治理中起着至关重要的作用。目前，监测通常是通过现场采样和/或通过建立监测站进行的，这可能是劳动密集型和昂贵的。最近，通过无人驾驶飞行器 (UAV) 和高光谱传感器进行遥感监测水质的可能性显示出巨大的希望，其主要优势在于更大的空间覆盖范围以及更高的光谱分辨率和更广泛的数据可能带来的更高准确度。相应地，需要建立更先进的方法来进行水质测定的高光谱分析，以利用这些丰富的信息。在这项研究中，一种称为最佳多波段比分析的分层贝叶斯模型聚合 (HBMA-OMBRA) 的新方法已被开发为从高光谱数据估算总悬浮固体 (TSS) 浓度的概念验证。该方法利用竞争模型的贝叶斯集成，因为没有适用于所有情况的最佳工作模型。它还包含一种称为集成频带比选择 (ENBRAS) 的新方法，用于通过一组集成和“装袋”程序识别最佳候选频带比 (BBR)，然后使用改进的 Batchelor Wilkin 算法对候选频带比进行聚类。本研究进行了一项实验室调查，以测量各种环境条件下不同实验中的高光谱反射率，以验证 HBMA-OMBRA 的稳健性。根据实验结果，使用 ENBRAS 确定了六个不同的候选 BBR 簇。特别是，红色、绿色和近红外光谱中的两个簇表现出最大的贡献。多聚类的重要性为先前文献中报告的对比结果提供了解释，并提供了一些证据来协调这些发现。