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Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

机译:来自加拿大不列颠哥伦比亚省的遥感海表温度和盐度产品与原位测量的比较

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Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 °C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.
机译:在考虑养殖和野生鱼类种群疾病的危险因素时,海面温度(SST)和盐度(SSS)是海洋和大气界面的基本变量。生态研究已经见证了使用数字和卫星技术(包括遥感工具)的最新趋势。我们探索了遥感SST和SSS数据的空间覆盖率,并将其与水温和盐度的原位测量结果进行了比较,从而建议对遥感数据进行调整,以用于水产养殖研究。原位数据来自2003年至2016年加拿大不列颠哥伦比亚省沿海地区的农场和野生监测点。同时从遥感产品中提取了SST和SSS值,并与20,513和20,038的水温和盐度原位记录进行了比较,分别来自232个不同站点。在评估的9种SST产品中,UKMO OSTIA SST(英国气象局)的检索率最高,一致性相关系数最高(0.86),一致性指数最高(0.93),缺失值最少,偏倚的均值和SD值最小,与原位测量相比。以UKMO OSTIA SST作为原位测量预测值的混合线性回归模型,估计UKMO OSTIA SST的调整系数为0.89°C。被评估的三种SSS产品都没有为原位提供适当的相应值,这表明当前缺乏研究区域的空间覆盖范围。这项研究表明,在SST产品中,UKMO OSTIA SST目前最适合于卑诗省沿海的水产养殖研究。这些数据经过估计的调整后几乎可以实时获得,从而可以将它们用于预测模型,病原体监视以及创建风险图。

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