Document Type
Article
Abstract
The application of satellite-derived sea surface temperature in coastal regions is critical for resolving the dynamics of frontal features and coastal upwelling. Here, we examine and compare sea surface temperature (SST) gradients derived from two satellite products, the Multi-Scale Ultra-High Resolution SST Product (MUR, 0.01° grid scale) and the Operational SST and Ice Analysis (OSTIA, 0.05° grid scale), available through the Group for High Resolution SST (GHRSST). Both products show similar seasonal variability, with maxima occurring in the summer time frame. Additionally, both products show an increasing trend of SST gradients near the coast. However, differences exist between the two products (maximum gradient intensities were around 0.11 and 0.06 °C/km for OSTIA and MUR, respectively). The potential contributions of both cloud cover and the collocation of the MUR SST onto the OSTIA SST grid product to these differences were examined. Spectra and coherences were examined at two specific latitudes along the coast where upwelling can occur. A major conclusion is that future work needs to focus on cloud cover and its impact on the derivation of SST in coastal regions. Future comparisons also need to apply collocation methodologies that maintain, as much as possible, the spatial variability of the high-resolution product.
Digital Object Identifier (DOI)
Publication Info
Published in Remote Sensing, Volume 17, Issue 15, 2025, pages 2722-.
Rights
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
APA Citation
Vazquez-Cuervo, J., García-Reyes, M., Wethey, D. S., Ciani, D., & Gomez-Valdes, J. (2025). Application and Comparison of Satellite-Derived Sea Surface Temperature Gradients to Identify Seasonal and Interannual Variability off the California Coast: Preliminary Results and Future Perspectives. Remote Sensing, 17(15), 2722–2722. https://doi.org/10.3390/rs17152722