Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164400
Title: Retrieval of total suspended solids concentration from hyperspectral sensing using hierarchical Bayesian model aggregation for optimal multiple band ratio analysis
Authors: Pak, Hui Ying
Law, Adrian Wing-Keung
Lin, Weisi
Keywords: Engineering::Environmental engineering
Issue Date: 2023
Source: Pak, H. Y., Law, A. W. & Lin, W. (2023). Retrieval of total suspended solids concentration from hyperspectral sensing using hierarchical Bayesian model aggregation for optimal multiple band ratio analysis. Journal of Hydro-Environment Research, 46, 1-18. https://dx.doi.org/10.1016/j.jher.2022.11.002
Journal: Journal of Hydro-Environment Research 
Abstract: 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.
URI: https://hdl.handle.net/10356/164400
ISSN: 1570-6443
DOI: 10.1016/j.jher.2022.11.002
Schools: Interdisciplinary Graduate School (IGS) 
School of Civil and Environmental Engineering 
School of Computer Science and Engineering 
Research Centres: Nanyang Environment and Water Research Institute 
Environmental Process Modelling Centre 
Rights: © 2022 International Association for Hydro-environment Engineering and Research. All rights reserved. This paper was published by Elsevier B.V. in Journal of Hydro-Environment Research and is made available with permission of International Association for Hydro-environment Engineering and Research.
Fulltext Permission: embargo_20250207
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles
IGS Journal Articles
NEWRI Journal Articles
SCSE Journal Articles

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