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Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products.

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region. [ABSTRACT FROM AUTHOR]