Week 5: Oceans and Us > Topic 5c - Citizen science

Satellite data offers a chance for the public, as well as scientists to get a bird’s eye view of the world. This has inspired new projects where the public collect data and help scientists working with Earth Observation to better understand the marine environment; including surfing to measure the temperature of coastal waters and identifying kelp forests in satellite images.

Remote sensing, especially sea surface temperature readings from satellites, are an essential part of conservation today, however these satellites and SST data only reads the skin of the ocean. When it comes to what is happening beneath the surface in situ measurements can be done by gathering data from public observation projects and crowd sourcing.

Project Hermes: The first global effort to measure ocean temperatures worldwide at the scale of the ecosystem. A community of divers worldwide source data revealing temperatures of the ocean, and other parameters important to understanding the underwater world.

PML Surfer Project: A project, which gathers SST measurements with surfers as the vehicle, rather than a boat or buoy. The coastal zone is a difficult area to measure, as these zones are high with life so sensors often get covered in algae or seaweed. Scientists use data gathered by surfers to create maps and time series, which provides them with information on distribution measurements along the coast.

Once you have a sufficient number of people taking measurements you can start to improve SST retrieval algorithms in those areas, especially with the improvements of satellite resolution, as we are starting to see with modern satellites.

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(a) Shows the time series of SST acquired by the surfer at Wembury beach overlain onto the daily median SST data from station L4 during the study period (N refers to the number of samples). (b) Shows a scatter plot of daily match-ups between SST acquired by the surfer at Wembury beach and SST data from station L4. Bracketed statistics refer to use of hourly match-ups between the two datasets. (c) Shows the time series of SST from satellite (AVHRR) at station L4 overlain onto the daily median SST data from station L4 (buoy) during the study period. (d) Shows a scatter plot of daily match-ups between SST from satellite (AVHRR) at station L4 and SST data from the buoy at station L4. (e) Shows the time series of SST from satellite (AVHRR) at Wembury beach overlain onto SST acquired by the surfer at Wembury beach during the study period. (f) Shows a scatter plot of daily match-ups between SST from satellite (AVHRR) at Wembury beach and SST data acquired by the surfer at Wembury beach. Statistics are denoted as follows: r2 is the squared Pearson correlation coefficient; Ψ is the Root Mean Square Error; Δ is the unbiased Root Mean Square Error; δ is the bias; S and I are the slope and intercept of a linear regression respectively; and N refers to the number of match-ups

(a) Shows the Tidbit V2 temperature logger attached at mid-point to the surfboard leash. HOBOware software and HOBO USB Optic Base Station (BASE-U-4) were used by the surfer to launch the Tidbit V2 temperature logger prior to each session, and then to upload data post session. (b) Shows the GARMIN extrex 10 GPS, water-resistant Aquapac and waist-bag worn by the surfer. Information at one second intervals on location (latitude and longitude), time, distance, speed and orientation for each surf, were extracted from the GPS device post session. (c) Shows the surfer equipped with the sensors, and (d) shows the surfer collecting data during a session at Wembury beach. Consent to publication was obtained from the participant in this figure.

(a) Wavelet analysis performed on monthly chlorophyll concentration field from Polcom/ERSEM model and (b) derived from MODIS sensor for April 2004