Probabilistic machine learning-based phytoplankton abundance using hyperspectral remote sensing
Remote sensing is a crucial tool for understanding the spatial dynamics of algal blooms by quantifying and detecting algal proliferation in water bodies. Hyperspectral remote monitoring enables precise pigment concentration measurements of Cyanobacteria, facilitating the accurate quantification of a...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2484864 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Remote sensing is a crucial tool for understanding the spatial dynamics of algal blooms by quantifying and detecting algal proliferation in water bodies. Hyperspectral remote monitoring enables precise pigment concentration measurements of Cyanobacteria, facilitating the accurate quantification of algal blooms with high spatial and spectral resolutions. However, current water quality management policies in the Republic of Korea predominantly rely on phytoplankton concentration to assess algal bloom status, which presents challenges for effective pigment estimation from remote sensing. To address this gap, this study employed airborne remote sensing using hyperspectral imagery and a deep-learning approach to directly estimate phytoplankton cell concentrations across extensive water bodies. Airborne monitoring was conducted to comprehensively capture the spatiotemporal features of algal dynamics from 2016 to 2022, complemented by concurrent in situ assessments of phytoplankton concentration, including Cyanobacteria, diatom, and Green algae. Utilizing a Bayesian neural network and natural gradient-boosting algorithm, we simulated phytoplankton abundance using airborne remote sensing data. The probabilistic models achieved test accuracy with coefficients of determination (R2) of approximately 0.6 and 0.4 for the cell concentration of different algal phyla, respectively. Furthermore, the algorithms provided spatial distributions of algal cell concentrations, enabling the identification of critical management zones for water quality. This study demonstrates that probabilistic deep learning algorithms can deliver timely and accurate phytoplankton concentrations, improving decision-making processes in water quality management. |
|---|---|
| ISSN: | 1548-1603 1943-7226 |