Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
i

Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters

Filetype[PDF-8.89 MB]


Select the Download button to view the document
This document is over 5mb in size and cannot be previewed
Details You May Also Like

Details:

  • Journal Title:
    GIScience & Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    NOS (National Ocean Service)
  • Description:
    This study examines the use of the Multi-Spectral Instrument (MSI) in Sentinel-2 satellite in combination with regression-based random forest models to estimate bathymetry along the extended southwestern Florida nearshore region. In this study, we focused on the development of a framework leading to a generalized Satellite-Derived Bathymetry (SDB) model applicable to an extensive and diversified coastal region (>200 km of coastline) utilizing multi-date images. The model calibration and validation were done using airborne lidar bathymetry (ALB). As ALB surveys are very expensive to conduct, the proposed model was trained with a limited and practically feasible ALB data sample to expand the model’s practicality. Out of the three different sub-models introduced using varying combinations of historical satellite imagery, the combined-band model with the largest feature pool yielded the highest accuracy. The results showed root mean square error (RMSE) values of 8% and lower for the 0–13.5 m depth range (limit of the lidar surveys used) for all areas of interest, indicating the model efficiency and adaptability to varying coastal characteristics. The influence of training sample locations on model performance was evaluated using three distinct model configurations. The difference between these configurations was less than 5 cm, which highlights the robustness of the proposed SDB model. The quality of the satellite imagery is a significant factor that influences the accuracy of the bathymetry estimation. A preliminary methodology incorporating spectral data embedded in Sentinel-2 imagery to effectively select the most optimal satellite imagery was also proposed in this study.
  • Keywords:
  • Source:
    GIScience & Remote Sensing, 59(1), 1143-1158
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA18NOS4000198
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.27.1