Expanding Aquatic Observations through Recreation

Brewin, Robert J. W.; Hyder, Kieran; Andersson, Andréas; Billson, Oliver; Bresnahan, Philip J.; Brewin, Thomas G.; Cyronak, Tyler; Dall’Olmo, Giorgio; Mora, L. De; Graham, George W.; Jackson, T. J.; Raitsos, Dionysios Ε.

doi:10.3389/fmars.2017.00351

Cited by 28 publications

(26 citation statements)

References 92 publications

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“…Here, we have demonstrated the value of using loggers deployed and maintained in shallow subtidal habitats, which allowed for a high number of match-ups with satellite-derived SST. Integrating these observations with other developing in situ techniques, such as citizen science [30][31][32][33]35], the tagging of marine vertebrates with sensors [71], coastal gliders [72] and autonomous beach buoy systems [73,74], may significantly enhance the spatial and temporal sampling of in situ data in the nearshore, and improve operational satellite SST retrievals.…”

Section: Forward Outlookmentioning

confidence: 99%

“…They found a significant reduction in the performance of AVHRR algorithms at retrieving SST at the coastline, with root mean square differences over twice that observed from validations using buoy data a few kilometres offshore. Although there is remarkable potential for using surfers and other watersports participants for improving data collection in the nearshore coastal ocean [31][32][33][34][35], currently such datasets are relatively sparse and limited to conditions preferable for the activity, such that the findings of Brewin et al [30] were based on a relatively limited number of satellite and in situ match-ups.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

et al. 2018

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Abstract:The nearshore coastal ocean is one of the most dynamic and biologically productive regions on our planet, supporting a wide range of ecosystem services. It is also one of the most vulnerable regions, increasingly exposed to anthropogenic pressure. In the context of climate change, monitoring changes in nearshore coastal waters requires systematic and sustained observations of key essential climate variables (ECV), one of which is sea surface temperature (SST). As temperature influences physical, chemical and biological processes within coastal systems, accurate monitoring is crucial for detecting change. SST is an ECV that can be measured systematically from satellites. Yet, owing to a lack of adequate in situ data, the accuracy and precision of satellite SST at the coastline are not well known. In a prior study, we attempted to address this by taking advantage of in situ SST measurements collected by a group of surfers. Here, we make use of a three year time-series (2014-2017) of in situ water temperature measurements collected using a temperature logger (recording every 30 min) deployed within a kelp forest (∼3 m below chart datum) at a subtidal rocky reef site near Plymouth, UK. We compared the temperature measurements with three other independent in situ SST datasets in the region, from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and from a group of surfers at two beaches near the kelp site. The three datasets showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected from the kelp site and the two autonomous buoys were matched with operational Advanced Very High Resolution Radiometer (AVHRR) EO SST passes, all within 1 h of the in situ data. By extracting data from the closest satellite pixel to the three sites, we observed a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square differences at the kelp site over twice that observed at the two offshore buoys. Comparing the in situ water temperature data with pixels surrounding the kelp site revealed the performance of the satellite data improves when moving two to three pixels offshore and that this improvement was better when using an SST algorithm that treats each pixel independently in the retrieval process. At the three sites, we related differences between satellite and in situ SST data with a suite of atmospheric variables, collected from a nearby atmospheric observatory, and a high temporal resolution land surface temperature (LST) dataset. We found that differences between satellite and in situ SST at the coastline (kelp site) were well correlated with LST and solar zenith angle; implying contamination of the pixel by land is the principal cause of these larger differences at the coastline, as opposed to issues with atmospheric correction. This contamination could be either from land directly within the pixel, potentially impacted by errors in geo-location, or possibly through thermal adjacenc...

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Section: Forward Outlookmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

et al. 2018

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“…Recreational fisheries are providing an increasing amount of data for researchers about their activity (e.g., Lloret et al, 2008;Giovos et al, 2018), but also about the ecosystems within they operate (e.g., Tiralongo et al, 2019), but the use of their FEK has been limited (see review by Hind, 2014). Given that there are many more recreational than commercial fishers (Hyder et al, 2018;Arlinghaus et al, 2019a), recreational fishers represent a relatively untapped source of long-term information on marine ecosystems (Brewin et al, 2017). Recreational FEK has been used to quantify variations in abundances and distribution of different fish stocks (e.g., Azzurro et al, 2011;Zukowski et al, 2011;Beaudreau and Levin, 2014;Sbragaglia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Use of Recreational Fishers’ Ecological Knowledge to Assess the Conservation Status of Marine Ecosystems

et al. 2020

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“…One solution gaining momentum is the integration of miniaturised environmental sensors into watersports equipment. New evidence has suggested that environmental monitoring in the nearshore could be drastically improved by harnessing vast numbers of citizens who partake in marine recreational sports [19]. To date, there have been studies looking at the use of divers [20][21][22], fishermen [23], stand-up paddle boarders [24], kayakers [25], sailors [26], and surfers [27,28].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Methods for Measuring Sea Surface Temperature When Surfing

Brewin

Cyronak

Bresnahan

et al. 2020

Oceans

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Nearshore coastal waters are among the most dynamic regions on the planet and difficult to sample from conventional oceanographic platforms. It has been suggested that environmental sampling of the nearshore could be improved by mobilising vast numbers of citizens who partake in marine recreational sports, like surfing. In this paper, we compared two approaches for measuring sea surface temperature (SST), an Essential Climate Variable, when surfing. One technique involved attaching a commercially-available miniature temperature logger (Onset UTBI-001 TidbiT v2) to the leash of the surfboard (tether connecting surfer and surfboard) and the second, attaching a surfboard fin (Smartfin) that contained an environmental sensor package. Between July 2017 and July 2018, 148 surfing sessions took place, 90 in the southwest UK and 58 in San Diego, California, USA. During these sessions, both Smartfin and leash sensors were deployed simultaneously. On the leash, two TidbiT v2 sensors were attached, one with (denoted LP) and one without (denoted LU) a protective boot, designed to shield the sensor from sunlight. The median temperature from each technique, during each surfing session, was extracted and compared along with independent water temperature data from a nearby pier and benthic logger, and matched with photosynthetically available radiation (PAR) data from satellite observations (used as a proxy for solar radiation during each surf). Results indicate a mean difference ( δ ) of 0.13 °C and mean absolute difference ( ϵ ) of 0.14 °C between Smartfin and LU, and a δ of 0.04 °C and an ϵ of 0.06 °C between Smartfin and LP. For UK measurements, we observed better agreement between methods ( δ = 0.07 °C and ϵ = 0.08 °C between Smartfin and LU, and δ = 0.00 °C and ϵ = 0.03 °C between Smartfin and LP) when compared with measurements in San Diego ( δ = 0.22 °C and ϵ = 0.23 °C between Smartfin and LU, and δ = 0.08 °C and ϵ = 0.11 °C between Smartfin and LP). Surfing SST data were found to agree well, in general, with independent temperature data from a nearby pier and benthic logger. Differences in SST between leash and Smartfin were found to correlate with PAR, both for the unprotected (LU) and protected (LP) TidbiT v2 sensors, explaining the regional differences in the comparison (PAR generally higher during US surfing sessions than UK sessions). Considering that the Smartfin is sheltered from ambient light by the surfboard, unlike the leash, results indicate the leash TidbiT v2 sensors warm with exposure to sunlight biasing the SST data positively, a result consistent with published tests on similar sensors in shallow waters. We matched all LU data collected prior to this study with satellite PAR products and corrected for solar heating. Results highlight the need to design temperature sensor packages that minimise exposure from solar heating when towed in the surface ocean.

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Expanding Aquatic Observations through Recreation

Cited by 28 publications

References 92 publications

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

The Use of Recreational Fishers’ Ecological Knowledge to Assess the Conservation Status of Marine Ecosystems

Comparison of Two Methods for Measuring Sea Surface Temperature When Surfing

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