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

Jorge Vazquez-Cuervo,Jose Gomez-Valdes,Chelle Gentemann,Luis Miranda,Tom Van Der Stocken,Marouan Bouali,Wenqing Tang

doi:10.3390/rs11171964

Abstract

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.

Highlights

As a motivating factor in the study, the application of remote sensing techniques for understanding coastal and open-ocean surface water properties is an area of active research, helping to better understand oceanic variability associated with mesoscale and submesoscale variability such as frontal structures, eddies, and meanders
In the Arctic Ocean, measurements from the Ball Experiment sea surface temperature (SST) (BESST) thermal infrared radiometer were compared against Moderate Resolution Imaging Spectroradiometer (MODIS) data and revealed significant spatial variability within 1 km pixels that were associated with density fronts in the marginal ice zone [15]
The SST patterns from Saildrone, Multi-Scale Ultra-High-Resolution (MUR), OSTIA, and Canadian Meteorological Center (CMC) showed a strong similarity with warmer waters off Southern California and the Baja coasts (Figure 1)

Summary

Introduction

As a motivating factor in the study, the application of remote sensing techniques for understanding coastal and open-ocean surface water properties is an area of active research, helping to better understand oceanic variability associated with mesoscale and submesoscale variability such as frontal structures, eddies, and meanders These features are associated with upwelling and downwelling, and have been recognized to play an important role in shaping physical and biogeochemical processes in the ocean [1] and influencing the spatiotemporal variability in primary productivity levels [2,3]. The validation of SST and SSS data has been achieved by direct comparisons with oceanic buoy measurements [6,7,8] This approach does not allow for determining how well remote sensing data is resolving the spatial variability at the mesoscale to submesoscale. SST analyses are likely to perform differently depending on the environmental conditions [6], stressing the need for independent coastal validation

Methods

Results

Discussion

Conclusion