Marine & Environmental Sciences Faculty Articles



Document Type


Publication Title

Continental Shelf Research



Publication Date



Coastal oceanography, Citizen science, Surfing, Sea surface temperature, Outreach


Coastal populations and hazards are escalating simultaneously, leading to an increased importance of coastal ocean observations. Many well-established observational techniques are expensive, require complex technical training, and offer little to no public engagement. Smartfin, an oceanographic sensor–equipped surfboard fin and citizen science program, was designed to alleviate these issues. Smartfins are typically used by surfers and paddlers in surf zone and nearshore regions where they can help fill gaps between other observational assets. Smartfin user groups can provide data-rich time-series in confined regions. Smartfin comprises temperature, motion, and wet/dry sensing, GPS location, and cellular data transmission capabilities for the near-real-time monitoring of coastal physics and environmental parameters. Smartfin's temperature sensor has an accuracy of 0.05 °C relative to a calibrated Sea-Bird temperature sensor. Data products for quantifying ocean physics from the motion sensor and additional sensors for water quality monitoring are in development. Over 300 Smartfins have been distributed around the world and have been in use for up to five years. The technology has been proven to be a useful scientific research tool in the coastal ocean—especially for observing spatiotemporal variability, validating remotely sensed data, and characterizing surface water depth profiles when combined with other tools—and the project has yielded promising results in terms of formal and informal education and community engagement in coastal health issues with broad international reach. In this article, we describe the technology, the citizen science project design, and the results in terms of natural and social science analyses. We also discuss progress toward our outreach, education, and scientific goals.








This work was funded by the Lost Bird Project and SDG&E. Robert Brewin was supported by a UKRI Future Leader Fellowship (MR/V022792/1) and REU students have been supported by the National Science Foundation (OCE-1637632 and IIS-1852403). E4E is supported in part by the National Science Foundation Grant IIS-1852403, UCSD Qualcomm Institute, UCSD Department of Computer Science and Engineering, UCSD Department of Electrical and Computer Engineering, and UCSD Halıcıoğlu Data Science Institute.

Additional Comments

Data availability All data are freely available at the open data portal sites referenced in the article

Peer Reviewed