Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019, Vol.11 (17), p.1964
• Main Authors: Vazquez-Cuervo, Jorge, Gomez-Valdes, Jose, Bouali, Marouan, Miranda, Luis, Van der Stocken, Tom, Tang, Wenqing, Gentemann, Chelle
• Format: Article
• Language: English
• Subjects: Coasts; Datasets; Diurnal; MODIS; Oceanography; Remote sensing; Remote sensing systems; saildrone; Salinity; Salinity effects; Satellites; sea surface salinity; Sea surface temperature; SMAP; Standard deviation; Studies; Surface vehicles; Unmanned vehicles; validation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11171964

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.