Accurate coastal bathymetry is critical for navigation, environmental monitoring, and marine resource management. This study presents a deep learning-based approach that fuses Sentinel-2 multispectral imagery with airborne hyperspectral-derived reference data to generate high-resolution satellite-derived bathymetry (SDB). To address the spatial resolution mismatch between Sentinel-2 (10 m) and LiDAR reference data (1 m), three interpolation methods—Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Spline—were employed to resample spectral reflectance data to a 1 m grid. Two spectral input configurations were evaluated: the log-ratio of Bands 2 and 3, and raw RGB composite reflectance (Bands 2, 3, and 4). A Fully Convolutional Neural Network (FCNN) was trained under each configuration and validated using LiDAR-based depth. The RGB + NN combination yielded the best performance, achieving an RMSE of 1.2320 m, MAE of 0.9381 m, bias of +0.0315 m, and R² of 0.6261, while the log-ratio + IDW configuration showed lower accuracy. Visual and statistical analyses confirmed the advantage of the RGB + NN approach in preserving spatial continuity and spectral-depth relationships. This study demonstrates that both interpolation strategy and input configuration critically affect SDB model accuracy and generalizability. The integration of spatially adaptive interpolation with airborne hyperspectral reference data represents a scalable and efficient solution for high-resolution coastal bathymetry mapping.

우주기술과 응용(Journal of Space Technology and Applications; J. Space Technol. Appl. 이하 JSTA )은 한국의 우주과학 및 우주 응용 분야에서 가장 권위 있는 학술단체인 한국우주과학회에서 Journal of Astronomy and Space Sciences 와 더불어 발행되고 있는 공식적인 정기 간행물이다. JSTA 는 2021년 5월 창간호를 시작으로 연 4회 분기별로 발간되고 있으며, JSTA 의 논문과 기고문 은 한국어 또는 영어로 게재 가능하며, 각 논문과 기고문에는 고유의 DOI가 부여된다. JSTA 에 게재 되는 모든 논문과 기고문은 Open Access 정책에 따라 JSTA 홈페이지(https://www.jstna.org)를 통해 누구나 전문을 무료로 열람할 수 있다.