Nearshore Wave Prediction for renewable energy: Initial Results with Remote Sensing and Buoy Data

Wave energy offers a promising source of clean and renewable power with wide-ranging applications. This paper focuses on the prediction of nearshore waves, aiming to understand their complex characteristics and behaviors. Accurate nearshore wave predictions are essential for activities like maritime planning and coastal development. Our study explores the relationship between Sentinel-2 Normalized Difference Water Index (NDWI) data and ground truth buoy measurements using linear regression. Additionally, we investigate the connection between Sentinel-1 Synthetic Aperture Radar (SAR) data and buoy measurements, employing the same linear regression approach. However, we find that linear regression, while useful, is not fully suited to tackle the complexity of nearshore wave prediction. The results indicate no relationship between NDWI values and buoy data. The SAR data exhibits an existing linear relationship with buoy data, however, it is a weak relationship. These findings underscore the potential of SAR for future research in this field. In the next phase of our research, we will further explore the use of Sentinel-1 SAR, either independently or in combination with Sentinel-3 altimeter technology, within a Regression Convolutional Neural Network (RCNN). This approach aims to better capture the nature of nearshore waves by combining the non-linear visual characteristics of the SAR images while also implementing the continuous data provided by the SAR and buoy data. In summary, our work contributes to understanding nearshore wave behavior and prediction. We emphasize the need for more advanced modeling techniques to address the intricate nature of nearshore wave prediction in the future.