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Suitability of remotely-sensed sea surface temperature for aquaculture research: comparison with in situ records from salmon farms in British Columbia (BC), Canada

Krishna Thakur1*, Crawford Revie1, Raphael Vanderstichel1 and Thitiwan Patanasatienkul1
  • 1 UPEI, Health Management, Canada

In recent years, ecosystem research has witnessed an increasing trend towards the use of digital and satellite technologies, such as remote-sensing tools [1], which detect and classify objects and/or surrounding areas on the earth’s surface, atmosphere, and oceans [2]. There is a plethora of freely-available remotely-sensed data, with many of them providing ocean measurements, including (among others) sea surface temperature (SST; e.g. nighttime, and daytime), salinity photosynthetically-available radiation (e.g. phytoplankton, chlorophyll). From the list of remotely-sensed data, some important environmental characteristics when considering disease in farmed and wild fish stocks include: sea surface temperature (SST) and salinity [3-5]. Environmental data (water temperature and salinity) for farmed salmon are recorded with reasonable regularity; however, these in-situ data have temporal gaps and missing values for many observations, while corresponding environmental data for wild salmon rarely exist. In the context of warming oceans, a recent study [3] has identified “salmon lice” as a candidate disease/pathogen, among several other marine diseases, for which a water temperature-based surveillance tool is highly recommended, and its authors suggest development of baseline models to link disease risk with water temperature. Remote-sensing tools are expected to contribute towards sustainable blue growth of the aquaculture sector by providing decision support to various end-users. The objectives of this study were to compare remotely-sensed SST with in-situ recorded water temperatures and calibrate the remotely-sensed data to adjust for differences. The in-situ data for water temperature were obtained from salmon farms and the BAMP research project (www.bamp.ca), captured during wild fish surveillance from 2011 to 2013 from sites located in the Broughton Archipelago and Nootka areas of coastal BC (Fig.1). Concurrent daily daytime and nighttime SST values for corresponding dates and sites were extracted from remotely-sensed SST measurements. The details of each of the satellite-derived SST products evaluated in this study are presented in Table 1. Since satellite derived (L3) SST products may have substantial missing values, we evaluated modelled products (L4) that combined several sources of SST data, including a number of satellite sources [6]. L4 (modelled) products are designed to provide the best available estimates of the SST from combined analyses of all available SST data; they are produced with global coverage, with minimal missing values, and provide foundation estimates (SSTfnd) free of diurnal variation [7, 8]. The relationship between in situ water temperature (at 1 m depth) and satellite-derived SST was evaluated by estimating the mean difference (bias) between the two measurements; standard deviation and root mean square of the bias; Pearson correlation coefficient and concordance correlation coefficient (CCC) [9]. The CCC provides an indication of agreement between the two measurements. In addition, a mixed linear regression model was fitted to predict the bias using one of the satellite-derived SSTs as the predictor and adjusting for month, year, and type (farm vs wild) of in situ measurement, with sampling sites as a random effect. A total of 20,276 in situ records for water temperature (at 1m depth) were available from 232 sites. However, due to cloud cover in the satellite images and the fact that some sites were beyond the coverage area of the satellite (32 sites), smaller number of matched observations were available for analysis. Among the different SST products evaluated, the modelled (L4) product, compiled by the UK Met Office (UKMO SST), was the best in terms of retrieval, with minimal missing values; as well, it had the smallest bias and standard deviation of the bias (Table 2). Also, the correlation was highest among other similar SST products and so was the CCC (above 0.85), with a noticeably strong linear relationship (Fig 2), with some dispersion, suggesting a high level of agreement between water temperature (at 1m depth) and SST values of UKMO data. The linear mixed model with UKMO SST as the predictor for the bias suggested a coefficient of 0.240C (95% CI: 0.21-0.27, p=0.000) for UKMO SST and an offset of -0.960C (p=0.000), along with adjustments for a significant effect of months (ranging from -1.62 to 0.20C), suggesting a seasonal pattern on the size of the bias. This study demonstrates that of the satellite-based SST products considered, UKMO SST was best suited for aquaculture studies in coastal BC. Since other available water temperature data sources have substantial numbers of missing values and temporal gaps, this study provides evidence that satellite data can complement, if not be substituted for, existing sources of data. Our study, in common with other published studies, suggests significant differences in agreement between satellite products across different regions [10-14], and similar studies for other aquaculture-producing areas may help to assess the suitability of SST products. The near real-time availability of these satellite-based data can be used in: forecast models, monitoring and surveillance of pathogens and wild stocks, and in creating risk maps. Table.1 Summary of the remotely-sensed and modelled SST products evaluated in the study Name of products Source Type Resolution Comments Availability** Missing data (%) Spatial Temporal Aqua SST 11 µ MODIS[15]/NASA! Satellite (L3) Daytime 95.4 Aqua NSST 11 µ ~4 km Daily Nighttime 2002 -present 94.6 Aqua SST4 4µ Nighttime 82.2 UKMO SST [8] UKMO!! Modelled (L4) ~6 km - 2006 to present 1.5 ODYSSEA SST [7] Myocean.eu 0.1 degree (~11 km) - 2007 to present 0* !Moderate Resolution Imaging Spectroradiometer, http://oceancolor.gsfc.nasa.gov/cgi/l3 !!Met office UK, **updated near real-time, *no coverage for >70% of in situ sites Table.2. Mean bias and correlations between remotely-sensed (L3 and L4) SST and in situ water temperature (at 1 m depth) from 2011 to 2013 SST Products n Mean bias(0C) SD of the bias(0C) Correlation CCC Aqua SST 11µ 848 0.60 2.78 0.820 0.698 Aqua NSST 11µ 832 -0.21 2.57 0.750 0.697 Aqua SST4 4µ 3702 -1.07 2.17 0.842 0.772 UKMO SST 15464 0.14 1.32 0.858 0.851 ODYSSEA SST 11416 1.67 3.12 0.253 0.199 Fig.1 Locations where in situ water temperature measurements and corresponding satellite-derived SST values were obtained, with inset map of BC Fig.2 Scatter plots of in situ water temperature (at 1m depth) to that of satellite-derived SSTs for sites in coastal BC

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Acknowledgements

We would like to thank Canada Research Chair in Aquatic Epidemiology for funding support for this study.

References

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Keywords: remote sensing, SST change, Aquatic epidemiology, Wild salmon, water temperature

Conference: AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.

Presentation Type: Oral

Topic: Aquatic Animal Epidemiology

Citation: Thakur K, Revie C, Vanderstichel R and Patanasatienkul T (2016). Suitability of remotely-sensed sea surface temperature for aquaculture research: comparison with in situ records from salmon farms in British Columbia (BC), Canada. Front. Vet. Sci. Conference Abstract: AquaEpi I - 2016. doi: 10.3389/conf.FVETS.2016.02.00028

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Received: 30 May 2016; Published Online: 14 Sep 2016.

* Correspondence: Dr. Krishna Thakur, UPEI, Health Management, Charlottetown, PE, Canada, thakurvet@gmail.com