SAR remote sensing for monitoring harmful algal blooms using deep learning models

Abstract

Harmful Algal Blooms (HABs) threaten aquatic ecosystems, necessitating effective monitoring strategies in water resource management. Satellite-based remote sensing has emerged as a popular method to address the limitations of in-situ monitoring. However, cloud covers can obstruct optical imagery, causing data loss. Synthesis Aperture Radar (SAR) imagery, with its capabilities, can penetrate through any weather conditions. We applied SAR imagery with the Faster Regional Convolutional Neural Networks (Faster R-CNN) model to detect the algal bloom. The dataset of the Geum River Basin was obtained from 2020 to 2022. The sigma naught values (dB) were analyzed from the SAR imagery to clarify the reflectance properties of algae in VH and VV polarizations. The values ranged between -12 dB and -33 dB and -5 dB and -27 dB for VH and VV polarization, respectively. The model was developed with hyperparameter optimization to detect the algal bloom by splitting the training from 2020 to 2022, and the testing dataset 2022. Evaluation metrics including precision, recall, and F1 scores yielded values of 0.600, 0.692, and 0.643, respectively. The developed model was simulated to identify the seasonal outbreak. The result illustrated that the algal blooms were detected only in the summer of 2021 and 2022. Furthermore, the model was validated in supporting an existing algal alert report, demonstrating the potential for real-time monitoring. Finally, this study highlights the effectiveness of employing SAR imagery with the Faster R-CNN model to develop an algorithm for detecting algal blooms, offering advancements in water management practices.