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Open AccessEditor’s ChoiceArticle

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

1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2
Physical Oceanography Department, Center for Scientific Research and Higher Education at Ensenada, Ensenada 22860, Baja California, Mexico
3
Institute of Oceanography, University of São Paulo, São Paulo 05508-120, Brazil
4
Earth and Space Research,2101 Fourth Avenue, Suite 1310, Seattle, Washington, WA 98121, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 1964; https://doi.org/10.3390/rs11171964
Received: 15 May 2019 / Revised: 10 August 2019 / Accepted: 15 August 2019 / Published: 21 August 2019
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
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. View Full-Text
Keywords: MODIS; oceanography; remote sensing; saildrone; sea surface salinity; sea surface temperature; SMAP; validation MODIS; oceanography; remote sensing; saildrone; sea surface salinity; sea surface temperature; SMAP; validation
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MDPI and ACS Style

Vazquez-Cuervo, J.; Gomez-Valdes, J.; Bouali, M.; Miranda, L.E.; Van der Stocken, T.; Tang, W.; Gentemann, C. Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast. Remote Sens. 2019, 11, 1964.

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