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

机译:从不列颠哥伦比亚,加拿大的远程感测海表面温度和盐度产品的比较

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摘要

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:考虑疾病的危险因素在养殖和野生鱼类时海面温度(SST)和盐度(SSS)是在海洋和大气的界面基本变量。生态研究见证了利用数字和卫星技术,包括遥感工具的近期走势。我们探讨遥感SST和SSS数据的空间覆盖,并在水温和盐度的现场测量,这导致了建议调整其在水产养殖研究利用遥感数据进行了比较。原位数据来自农场和野生监测点在沿海加拿大不列颠哥伦比亚省,从2003年到从遥感产品中提取,并在水温和盐度现场记录20513和20038相比2016年同期SST和SSS值,分别从232个不同的网站。在评估9个SST产品,英国气象局OSTIA SST(英国气象局)具有最高的检索和最一致的相关系数(0.86),协议的最高指数(0.93),最少的缺失值和最小的平均值和SD值偏差,相比于现场测量。的混合线性回归模型UKMO OSTIA SST作为预测用于原位测量估计的0.89℃下UKMO OSTIA SST的调整系数。三种SSS产品的无评估了原位网站提供适当的相应值,表明了研究区空间范围是目前所缺乏的。这项研究表明,SST产品中,英国气象局OSTIA SST目前最适合在沿海BC水产养殖研究。这些数据与估计调整的近实时可用性将允许他们在预测模型,病原体监测使用和风险地图的创建。

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