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🛰 Satellite(s)

e.g., Sentinel-2.

📊 Monitoring element

Land surface reflectance.

🧰 Description technique

Process Li et al. (2021) processed Sentinel-2 reflectance imagery to build 'clean' coastal water images, calculate . They calculated Normalized Difference Water Index, correct corrected it for sea-air surface effect , and calculate estimated bathymetry.

📏 Accuracy / Resolution

Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m (results from testing at 6 globally diverse sites); 10 m spatial resolution.

🗺 Case study

Li et al. (2021) provide multiple examples: Heron Island, Australia; West Coast of Hawai’i Island, Hawai’i; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines.
Google Earth Engine code available:

🔗 https://github.com/CoralMapping/GEE_Sentinel2_Bathymetry_Paper

🧩 Also fits domain

Freshwater

Tip

Benefits

  • Shallow bathymetry information is useful e.g., for understanding and characterising coastal environments, benthic habitats mapping and monitoring, planning marine operations and transportation.

  • Monitoring can be done via Google Earth Engine.

  • High spatial resolution (10 m).

Note

Limitations

  • Images with minimal cloud cover, sun glint, and water turbidity are required.

  • Cloud masking may be required.

Info

Applicability for Northland

Yes, probably.

Testing with bathymetry maps would confirm relevance/accuracy.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature, and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication references

📚 Li J, Knapp DE, Lyons M, Roelfsema C, Phinn S, Schill SR, Asner GP. 2021. Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine. Remote Sensing. 13(8):1469.

🔗 https://www.mdpi.com/2072-4292/13/8/1469

Other references

📚 Ashphaq M, Srivastava PK, Mitra D. 2021. Review of near-shore satellite derived bathymetry: classification and account of five decades of coastal bathymetry research. Journal of Ocean Engineering and Science.

🔗 https://www.sciencedirect.com/science/article/pii/S2468013321000218

📚 Yeu Y, Yee J-J, Yun HS, Kim KB. 2018. Evaluation of the Accuracy of Bathymetry on the Nearshore Coastlines of Western Korea from Satellite Altimetry, Multi-Beam, and Airborne Bathymetric LiDAR. Sensors (Basel, Switzerland). 18(9):2926. doi:10.3390/s18092926.

🔗 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164467/

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