Shallow water bathymetry (< 20m depth)

Satellite(s) e.g., Sentinel-2.	Monitoring element Land surface reflectance.

Satellite(s)

e.g., Sentinel-2.

Monitoring element

Land surface reflectance.

Description technique

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

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).

Limitations

Images with minimal cloud cover, sun glint, and water turbidity are required.
Cloud masking may be required.

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/