Satellite-derived Sea Surface Temperature Data Research Articles

Characteristics of Marine Heatwaves in the Southeastern Baltic Sea Based on Long-Term In Situ and Satellite Observations

Marine heatwaves (MHWs) are known to pose a threat to aquatic ecosystems and coastal communities and, as a result, they receive significant attention nowadays, thus motivating our scientific interest in better understanding the regional patterns of these events. In this study, we analyze MHWs in the SE part of the Baltic Sea, defining them as anomalously warm water events, where the water temperature exceeds the 90th percentile threshold of the corresponding calendar day for at least five or more consecutive days. Our study is based on a combination of long-term (1993–2023) in situ data, field measurements, and satellite-derived sea surface temperature data during the warm (May–August) period. Study results suggest that although short-lived (5–9 days) MHW events typically dominate throughout the analyzed period, the occurrences of longer (more than three weeks) and more intense MHWs have increased in our study region in the recent decade. The heatwaves are observed both in coastal and open waters, with SST anomalies up to around 5–6 °C above the 90th percentile threshold during extreme events, extending thermal influence as deep as 20 m during prolonged and more intense events. We believe that the results of this study contribute to a better understanding of MHW patterns in the study region, which is important from an ecological and socio-economic point of view, providing valuable insights for human health aspects as well.

Marine Heat Waves over Natural and Urban Coastal Environments of South Florida

Marine Heat Wave (MHW) events are increasingly recognized as an important factor in the sustainability of coastal environments (both natural and urban), in the context of climate change. They are related to increasing trends of Sea Surface Temperature (SST) at the adjacent ocean waters. SST is an important parameter of the earth’s climate and increasing SST trends have been associated with adverse effects on coastal ecosystems, with important environmental and socioeconomic implications. This study focuses on the SST interannual variability over the coastal marine environment of South Florida, which contains several fragile ecosystems, and draws associate effects with adjacent large urban coastal settlements. The methodology is based on high-resolution satellite-derived SST data during a 40-year period (1982–2021), augmented by recent high-resolution model simulations (2012–2020). A generally increasing trend has been detected in the observations over the entire region (0.19 °C/decade). The unusual temperature levels have been associated with the formation of extensive MHW events, which showed interannual positive trends (0.75 events/decade) during the 40-year study period. Specifically, the six most recent years (2015–2021) were characterized by the strongest formation of MHWs with a peak in 2015, 2019 and 2020, with more than 8 events/year and approximately 70 to 110 days/year duration in total. The Florida Keys, especially along the Straits of Florida (southern island coasts), revealed very strong increasing trends. Miami Beach is also characterized by strong interannual trends (1.1 events/decade and 10 days/decade) compared to the enclosed basin of Biscayne Bay. In addition to the influence of atmospheric conditions over all regions around South Florida, the formation of MHWs near the eastern Florida coasts was also controlled by ocean dynamics, related to the warm Florida Current (FC). The evolution of the FC close to the eastern coasts (e.g., Miami Beach) was found to be a pre-condition of MHW formation. Several disastrous events on the biotic environment of South Florida near large urban settlements have been related to the formation of MHWs. The detected positive trends, and especially the recent high peaks of MHW events, may enhance the loss of specific heat-sensitive species, damaging the biodiversity of this tropical coastal environment and weakening the natural coastal protection against tropical storms. Urban planning for sustainable development in South Florida’s coastal cities must take into account MHW trends.

Sea Surface Temperature Variability and Marine Heat Waves over the Aegean, Ionian, and Cretan Seas from 2008–2021

The sea surface temperature (SST) is an important factor and indicator of the sea water quality, with various ecological and anthropogenic implications. We used high-resolution satellite-derived SST data, in tandem with field observations and long-term meteorological data, to investigate the spatial and interannual SST variability over the Aegean, Ionian, and Cretan (AIC) Seas during the recent 14-year period (2008–2021). Increasing trends were identified for most of the sub-basins of the AIC Seas. The numbers and durations (days) of the marine heat waves (MHWs) have significantly increased, especially during the last quadrennial period (2018–2021). Changes have been detected in both the maximum and minimum values; however, the trend of the mean annual values is mainly associated with the interannual increases in the lowest values (weaker minima during the cold seasons). The interannual variability and the increasing positive trends of the air temperature are very similar to the SST variations, showing a 5-to-10-day lag between the seasonal time series of the two parameters for all regions; however, extreme atmospheric events (e.g., cold fronts or heat waves) have a more direct impact on the SST variability (zero lag). MHWs were more frequent over the northern Aegean Sea, especially in Thermaikos Gulf, which is characterized as a “hot spot” for MHWs. MHWs were rarer over the southern regions, especially over the southeastern Aegean and Cretan Seas. A stratified upper ocean, controlled by buoyant brackish plumes, such as the Black Sea Waters (BSW) in the northern Aegean, may increase the heat storage capacity of the surface water masses, contributing to the further warming of the ocean. This was the case in the summer of 2021, which was a unique year for the AIC Seas, and especially for the northern Aegean, which revealed the highest SST values among all the study years. The satellite-derived observations of the 2008–2021 period showed increasing trends for all coastal waters, strong trend slopes for most of the coasts of the northern Aegean and central Ionian Seas, and milder trend slopes in the eastern Aegean.

Ocean Front Detection with Glider and Satellite-Derived SST Data in the Southern California Current System

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.

Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model

In this work, we estimate extreme sea surface temperature (SST) hotspots, that is, high threshold exceedance regions, for the Red Sea, a vital region of high biodiversity. We analyze high-resolution satellite-derived SST data comprising daily measurements at 16,703 grid cells across the Red Sea over the period 1985–2015. We propose a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture spatially-varying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture (DPM) of low-rank spatial Student’s t processes (LTPs). By specifying cluster-specific parameters for each LTP mixture component, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately. Our proposed model has a nonstationary mean, covariance, and tail dependence, and posterior inference can be drawn efficiently through Gibbs sampling. In our application, we show that the proposed method outperforms some natural parametric and semiparametric alternatives. Moreover, we show how hotspots can be identified, and we estimate extreme SST hotspots for the whole Red Sea, projected until the year 2100, based on the Representative Concentration Pathways 4.5 and 8.5. The estimated 95% credible region, for joint high threshold exceedances include large areas covering major endangered coral reefs in the southern Red Sea.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies.

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

Application of remotely sensed sea surface temperature for assessment of recurrent coral bleaching (2014–2019) impact on a marginal coral ecosystem

The 2014–2016 El Niño Southern Oscillation (ENSO) caused a prolonged marine heatwave that led to widespread coral bleaching and mortality across the Indo-pacific coral reefs. Prediction of coral bleaching and assessment of bleaching impact on corals is vital for reef ecosystem functioning, services and management. Wherein, advanced satellite remote sensing approach to determine and quantify the thermal stress on corals can assist as an alternative and convenient tool for reef monitoring programs. The present study examines the impact of consecutive coral bleaching episodes on shallow-water marginalized patch reef ecosystems on the Eastern Arabian Sea. Advanced Very High-Resolution Radiometer (AVHRR) satellite data from the NOAA Coral Reef Watch’s (NOAA-CRW) platform, known as CoralTemp, were used to analyze the thermal stress on the coral reefs. Coral bleaching indices like Bleaching Threshold (BT), Positive Anomaly (PA), and Degree Heating Weeks (DHW) were calculated. Ground-truthing revealed that detected thermal stress from satellite-derived Sea Surface Temperature (SST) data over this region well corroborate with the mass coral bleaching events, and found reliable for detecting coral bleaching episodes in the marginalized turbid coral habitats. This study signifies the potential benefit of incorporating remote sensed SST data in coral bleaching monitoring program, which may guide to undertake targeted coral surveys and aid in decision-making and conservation of the vulnerable coral reef ecosystems.

Enhancing Information Content in the Satellite-Derived Daytime Infrared Sea Surface Temperature Dataset Using a Transformative Approach

The operational daytime Sea Surface Temperature (SST) retrieval from remote sensing Infrared (IR) measurements does not conventionally use Mid-Wave IR (MWIR) channels due to solar contamination. However, MWIR channels are desirable to obtain unambiguous surface information and a transformative approach, Physical Deterministic Sea Surface Temperature (PDSST) retrieval scheme, including MWIR channels, to enhance the information content in satellite-derived SST data. This paper mainly emphasizes on the quality and availability of swath-processed daytime SSTs from MODIS-AQUA radiances using PDSST suite, including MWIR channels, for coastal and near-coastal areas, which are needed the most. The focus areas of this study are the California coast, which is a highly dynamic oceanographic region, the Indian Ocean, which has a sparse population of in-situs, and the Chesapeake Bay, which is best known for seafood production. Apart from in-situ validation using iQuam (NOAA), indirect validation, by comparing different SST products, is also performed. The results of PDSST-suite are compared with the currently operational MODIS-AQUA SSTs, obtained from the NASA website, and microwave SSTs from AMSR2, obtained from RSS website. Both the operational products are based on the regression method. It is found that the PDSST suite including MWIR channels can extract 3-5 times as much information as the currently operational NASA-produced regression-based daytime SSTs from MODIS-AQUA radiances for coastal areas.

Spatial and temporal variability of sea surface temperature during El-Niño Southern Oscillation and Indian Ocean Dipole in the Strait of Malacca and Andaman Sea

This study examined the long-term trends and the impacts of climate variability caused by El-Niño Southern/Oscillation (ENSO) and Indian Ocean Dipole (IOD) by using 33-year Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST). The data were retrieved from year 1982 to 2014, hence enabled the observation of both ENSO and IOD events based on this long-term SST trends in the Strait of Malacca (SoM) and Andaman Sea. The 4-km resolution of satellite-derived SST data had been gathered from the National Oceanic and Atmospheric Administration (NOAA). Geographical location portrayed that the seasonal cycle of SST in the SoM and Andaman Sea was characterised by the Asian Monsoon. The correlations between monthly SST in regions with wind velocity, SST anomaly, and indices (Oceanic Niño Index (ONI) and Dipole Mode Index (DMI)) were analysed based on wavelet coherence (WTC) analysis. The seasonal SST in SoM revealed that the temperature was above normal trend during the southwest monsoon, wherein the region experienced warmer temperature especially during the ENSO period due to seasonal wind velocity. The results demonstrated that in certain years, such as in 1992, ENSO was dominating while in 1994, IOD was influential. During the strong El-Niño event in 1997, ONI did not correlate with SST trends in the SoM, whereas during positive IOD in 1994 revealed that DMI did not correlate with SST trends in both SoM and Andaman Sea. The warming trend seemed to have significantly increased over the past 33 years, as displayed by the continuous increment in temperature throughout the years in the study region.

The Rarity of Depth Refugia from Coral Bleaching Heat Stress in the Western and Central Pacific Islands

Some researchers have suggested that corals living in deeper reefs may escape heat stress experienced by shallow corals. We evaluated the potential of deep coral reef refugia from bleaching stress by leveraging a long record of satellite-derived sea surface temperature data with a temporal, spatial, and depth precision of in situ temperature records. We calculated an in situ stress metric using a depth bias-adjusted threshold for 457 coral reef sites among 49 islands in the western and central Pacific Ocean over the period 2001–2017. Analysis of 1,453 heating events found no meaningful depth refuge from heat stress down to 38 m, and no significant association between depth and subsurface heat stress. Further, the surface metric underestimated subsurface stress by an average of 39.3%, across all depths. Combining satellite and in situ temperature data can provide bleaching-relevant heat stress results to avoid misrepresentation of heat stress exposure at shallow reefs.

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

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.

A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data

Sea surface temperature (SST) is a vitally important parameter of the global ocean, which can profoundly affect the climate and marine ecosystems. To achieve an accurate and holistic prediction of the short and mid-term SST field, a spatiotemporal deep learning model is proposed which can capture the correlations of SST across both space and time. The model uses the convolutional long short-term memory (ConvLSTM) as the building block and is trained in an end-to-end manner. Experiments using 36-year satellite-derived SST data in a subarea of the East China Sea demonstrate that the proposed model outperforms the persistence model, the linear support vector regression (SVR) model, and two LSTM models with different settings, when judged using multiple statistics and from different perspectives. The results suggest that the proposed model is highly promising for short and mid-term daily SST field prediction accurately and conveniently.

Abnormal events detection using deep neural networks: application to extreme sea surface temperature detection in the Red Sea

We present a method based on deep learning for detecting and localizing abnormal/extreme events in sea surface temperature (SST) of the Red Sea images using training samples of normal events only. The method operates in two stages; the first one involves features extraction from each patch of the SST input image using the first two convolutional layers extracted from a pretrained convolutional neural network. In the second stage, two methods are used for training the model from the normal training data. The first method uses one-class support vector machine (1-SVM) classifier that allows a fast and robust abnormal detection in the presence of outliers in the training dataset. In the second method, a Gaussian model is defined on the Mahalanobis distances between all normal training data. Experimental tests are conducted on satellite-derived SST data of the Red Sea spanning for a period of 31 years (1985–2015). Our results suggest that the Gaussian model of Mahalanobis distances outperformed 1-SVM by providing better performance in terms of sensitivity and specificity.

Gambierdiscus and Fukuyoa species in the greater Caribbean: Regional growth projections for ciguatera-associated dinoflagellates

Satellite-derived sea surface temperature data from the MODIS Aqua instrument were used to project rates of growth for four Gambierdiscus and one Fukuyoa species, dinoflagellates associated with ciguatera fish poisoning. Dinoflagellate growth rates were modelled using experimental temperature vs. growth equations and projected bottom temperatures with light penetration and bathymetry masks. Daily projected growth rates for each species were used to calculate monthly, yearly and multiyear averages between 2003 and 2013. The resulting projections were then used to characterize patterns of regional Gambierdiscus/Fukuyoa abundance and compare these to ciguatera fish poisoning incidences in the Greater Caribbean Region. Model output indicated the highest growth potential was in the shelf waters of the Caribbean Sea, with moderate growth in the Bahamas, southern Florida and Gulf of Mexico; the lowest growth potential was in the northern Gulf of Mexico and along the U.S. south Atlantic coast. Mean projected growth rates generally coincided with distribution of ciguatera fish poisoning incidences in the region, with some exceptions in the southwestern Caribbean. The results of this study indicate spatial differences in Gambierdiscus and Fukuyoa growth likely play a prominent role in governing the occurrence of ciguatera fish poisoning in the greater Caribbean. Growth and distribution models may be an effective tool for ciguatera risk assessment.

Spatio-Temporal Decomposition of Satellite-Derived SST–SSH Fields: Links Between Surface Data and Ocean Interior Dynamics in the Agulhas Region

In this paper, we develop a novel observation-driven methodology to explore spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST) fields. Level-set-based and registration-based criteria are defined to evaluate and detect spatial links between SSH and SST anomaly fields. The method is applied to one-year SST and SSH time series in the highly dynamical Aghulas return current region. The analysis evidences a seasonal variation of the overall correlation between SST and SSH fields. As further revealed, the coldest SST anomalies are reported to efficiently trace the lowest SSH anomalies for all seasons, while the warmest SST anomalies solely match the largest SSH anomalies during winter. The second criterion relies on the registration of SSH and SST anomaly fields. The registration energy is shown to corresponds to the seasonal influence of the mixed-layer depth revealed here by atmospheric forcing. These results show us that the SST-derived SSH reconstruction using the surface quasigeostrophic approximation should take into account stratification effects, especially during summer. As discussed, the proposed methodology can enforce the reconstruction of sea surface current from a joint analysis of satellite altimetry data and high-resolution satellite-derived SST data.

The effect of sea surface temperature increase on the potential habitat of Ommastrephes bartramii in the Northwest Pacific Ocean

In the Northwest Pacific Ocean, the squid jigging fisheries from China, Japan and other countries and regions have targeted the west winter-spring cohort of neon flying squid (Ommastrephes bartramii) from August to November since the 1970s. This squid is a short-lived ecological opportunist with a life-span of about one year, and its population is labile and recruitment variability is driven by the environment or climate change. This variability provides a challenge for ones to forecast the key habitats affected by climate change. The catch data of O. bartramii from Chinese squid jigging fishery and the satellite-derived sea surface temperature (SST) data are used in the Northwest Pacific Ocean from August to November of 1998 to 2004, the SST preferences of O. bartramii corresponding to high values of catch per fishing day (CPUE) are determined and monthly potential habitats are predicted using a histogram analysis of the SST data. The possible changes in the potential habitats of O. bartramii in the Northwest Pacific Ocean are estimated under four climate change scenarios based on the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change, i.e., 0.5, 1, 2 and 4°C increases in the SST because of the climate change. The results reveal an obvious poleward shift of the potential habitats of O. bartramii in the Northwest Pacific Ocean.

A Robust Fixed Rank Kriging Method for Improving the Spatial Completeness and Accuracy of Satellite SST Products

Sea surface temperature (SST) plays a vital role in the Earth's atmosphere and climate systems. Complete and accurate SST observations are in great demand for forecasting tropical cyclones and projecting climate change. Satellite remote sensing has been used to retrieve SST globally, but missing values and biased observations impose difficulties on practical applications of these satellite-derived SST data. Conventional spatial statistics methods such as kriging have been widely used to fill the gaps. However, when such conventional methods are used to analyze a massive satellite data set of size $n$ , the inversion of the $n\times n$ covariance matrix may require $O(n^3) $ computations, which make the computation very intensive or even infeasible. The fixed rank kriging (FRK) performs dimension reduction through multiresolution wavelet analysis so that it can dramatically reduce the computation cost of various kriging methods. However, the FRK cannot directly be used for incomplete data over spatially irregular regions such as SSTs, and the potential bias in the satellite data is not addressed. In this paper, we construct a data-driven bias-correction model for the correction of the bias in satellite SSTs and develop a robust FRK (R-FRK) method so that the dimension reduction can be used to the satellite data in irregular regions with missing data. We implement the bias-correction model and the R-FRK to the level-3 mapped night Moderate Resolution Imaging Spectroradiometer SSTs. The accuracy of the resulting predictions is assessed using the colocated drifting buoy SST observations, in terms of mean bias (bias), root-mean-squared error, and R squared (R2) . The spatial completeness is assessed by the availability of ocean pixels. The assessment results show that the spatially complete SSTs with high accuracy can be obtained through the bias-correction model and the R-FRK method developed in this paper.

Reconstruction of Satellite-Derived Sea Surface Temperature Data Based on an Improved DINEOF Algorithm

An improved data interpolating empirical orthogonal function (I-DINEOF) algorithm was proposed in this study. Compared with the ordinary DINEOF algorithm, in the I-DINEOF algorithm, the existing data are not necessary to be selected for cross-validation and the initial matrix is directly used for reconstruction. Instead of using single EOF to reconstruct the whole spatio-temporal matrix, the initial matrix is divided into several subareas and each subarea is reconstructed by the most suitable EOF. To validate the accuracy of the I-DINEOF algorithm, a real sea surface temperature (SST) data set and three synthetic data sets with different missing data percentage are reconstructed by using the DINEOF and I-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF algorithm, the I-DINEOF algorithm is less affected by the missing data and can significantly enhance the accuracy of reconstruction.

Monitoring red tide with satellite imagery and numerical models: A case study in the Arabian Gulf

A red tide event that occurred in August 2008 in the Arabian Gulf was monitored and assessed using satellite observations and numerical models. Satellite observations revealed the bloom extent and evolution from August 2008 to August 2009. Flow patterns of the bloom patch were confirmed by results from a HYCOM model. HYCOM data and satellite-derived sea surface temperature data further suggested that the bloom could have been initiated offshore and advected onshore by bottom Ekman layer. Analysis indicated that nutrient sources supporting the bloom included upwelling, Trichodesmium, and dust deposition while other potential sources of nutrient supply should also be considered. In order to monitor and detect red tide effectively and provide insights into its initiation and maintenance mechanisms, the integration of multiple platforms is required. The case study presented here demonstrated the benefit of combing satellite observations and numerical models for studying red tide outbreaks and dynamics.

A comparison of satellite-derived sea surface temperature fronts using two edge detection algorithms

Satellite-derived sea surface temperature (SST) fronts provide a valuable resource for the study of oceanic fronts. Two edge detection algorithms designed specifically to detect fronts in satellite-derived SST fields are compared: the histogram-based algorithm of Cayula and Cornillon (1992, 1995) and the entropy-based algorithm of Shimada et al. (2005). The algorithms were applied to 4 months (July and August for both 1995 and 1996) of SST fields and the results are compared with SST data taken by the M.V. Oleander, a container ship that makes weekly transits between New York and Bermuda. There is no significant difference in front pixels found with the Cayula-Cornillon algorithm and those found in the in situ (Oleander) data. Furthermore, for strong fronts, with gradients greater than 0.2K/km, the distribution of fronts found with the Shimada et al. algorithm is quite similar to that of fronts found with the Cayula-Cornillon algorithm. However, there are significant differences in the number of weak fronts found. This is seen clearly in waters south of the Gulf Stream where the gradient magnitude of fronts found is less than 0.1K/km. In this region, the probability that the Shimada et al. algorithm detects a front rarely falls below 4% while neither the Cayula-Cornillon algorithm applied to the satellite-derived SST fields nor the gradient-based algorithm applied to the Oleander temperature time series find fronts more than 1% of the time. These results raise the question of exactly what qualifies as an SST front, a classic problem in edge detection.