Combined remote-sensing, model, and in situ measurements of sea surface temperature as an aid to recreational navigation: crossing the Gulf Stream

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

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A Multivariate Regression Approach to Adjust AATSR Sea Surface Temperature to In Situ Measurements

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2008.2006568 ◽

2009 ◽

Vol 6 (1) ◽

pp. 8-12 ◽

Cited By ~ 4

Author(s):

Pierre Tandeo ◽

Emmanuelle Autret ◽

Jean FranÇois Piolle ◽

Jean Tournadre ◽

Pierre Ailliot

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Multivariate Regression ◽

In Situ Measurements ◽

Sea Surface ◽

Regression Approach

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Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments

Remote Sensing ◽

10.3390/rs12111839 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1839 ◽

Cited By ~ 1

Author(s):

Jorge Vazquez-Cuervo ◽

Jose Gomez-Valdes ◽

Marouan Bouali

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

North Atlantic ◽

Gulf Stream ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Temporal Sampling ◽

The North

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.

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Reconstruction of super-resolution ocean pCO2 and air–sea fluxes of CO2 from satellite imagery in the southeastern Atlantic

Biogeosciences ◽

10.5194/bg-12-5229-2015 ◽

2015 ◽

Vol 12 (17) ◽

pp. 5229-5245 ◽

Cited By ~ 4

Author(s):

I. Hernández-Carrasco ◽

J. Sudre ◽

V. Garçon ◽

H. Yahia ◽

C. Garbe ◽

...

Keyword(s):

Surface Temperature ◽

Partial Pressure ◽

Sea Surface Temperature ◽

Ocean Color ◽

Super Resolution ◽

In Situ Measurements ◽

Sea Surface ◽

Co2 Fluxes ◽

Partial Pressure Of Co2

Abstract. An accurate quantification of the role of the ocean as source/sink of greenhouse gases (GHGs) requires to access the high-resolution of the GHG air–sea flux at the interface. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO2 ( pCO2) and air–sea CO2 fluxes at super resolution (4 km, i.e., 1/32° at these latitudes) using sea surface temperature (SST) and ocean color (OC) data at this resolution, and CarbonTracker CO2 fluxes data at low resolution (110 km). Inference of super-resolution pCO2 and air–sea CO2 fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the microcanonical multifractal formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multi-resolution analysis performed on the signal of the so-called singularity exponents allows for the correct and near optimal cross-scale inference of GHG fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO2 improves and enhances the description provided by CarbonTracker, capturing the small-scale variability. We examine different combinations of ocean color and sea surface temperature products in order to increase the number of valid points and the quality of the inferred pCO2 field. The methodology is validated using in situ measurements by means of statistical errors. We find that mean absolute and relative errors in the inferred values of pCO2 with respect to in situ measurements are smaller than for CarbonTracker.

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