Half a century of satellite remote sensing of sea-surface temperature

Minnett, Peter J.; Alvera‐Azcárate, Aida; Chin, T. M.; Corlett, Gary K.; Gentemann, Chelle; Karagali, Ioanna; Li, X.; Marsouin, A.; Marullo, Salvatore; Maturi, Eileen; Santoleri, Rosalia; Picart, Stéphane Saux; Steele, Michael; Vázquez-Cuervo, Jorge

doi:10.1016/j.rse.2019.111366

Cited by 219 publications

(196 citation statements)

References 357 publications

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“…Satellite observations have the unique capability (compared to in situ measurements) to provide systematic global and/or regional SST synoptic coverage at high spatial and temporal resolution, i.e., from one to a few kilometers and from hourly to daily frequency. SST has been measured from space over cloud free areas with relatively high accuracy (~0.5 • C) with the first 5-channel Advanced Very High Resolution Radiometer (AVHRR) since late 1981 [38] .…”

Section: Sst Covering the Satellite Era: Cmems Mediterranean Datasetmentioning

confidence: 99%

New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations

et al. 2020

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Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and the adjacent Northeastern Atlantic box (west of the Strait of Gibraltar) over the last 37 years, by using a satellite-based dataset from the Copernicus Marine Environment Monitoring Service (CMEMS). We found a mean warming trend of 0.041 ± 0.006 ∘ C/year over the whole Mediterranean Sea from 1982 to 2018. The trend has an uneven spatial pattern, with values increasing from 0.036 ± 0.006 ∘ C/year in the western basin to 0.048 ± 0.006 ∘ C/year in the Levantine–Aegean basin. The Northeastern Atlantic box and the Mediterranean show a similar trend until the late 1990s. Afterwards, the Mediterranean SST continues to increase, whereas the Northeastern Atlantic box shows no significant trend, until ~2015. The observed change in the Mediterranean Sea affects not only the mean trend but also the amplitude of the Mediterranean seasonal signal, with consistent relative increase and decrease of summer and winter mean values, respectively, over the period considered. The analysis of SST changes occurred during the “satellite era” is further complemented by reconstructions also based on direct in situ SST measurements, i.e., the Extended Reconstructed SST (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), which go back to the 19th century. The analysis of these longer time series, covering the last 165 years, indicates that the increasing Mediterranean trend, observed during the CMEMS operational period, is consistent with the Atlantic Multidecadal Oscillation (AMO), as it closely follows the last increasing period of AMO. This coincidence occurs at least until 2007, when the apparent onset of the decreasing phase of AMO is not seen in the Mediterranean SST evolution.

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Section: Sst Covering the Satellite Era: Cmems Mediterranean Datasetmentioning

confidence: 99%

New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations

et al. 2020

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“…In the recent past, many studies have been carried out by the scientific community worldwide to estimate the SST efficiently and accurately (e.g., a review by [13]). The satellite radiometer measurements acquired in the infrared (IR) and microwave (MW) region of the electromagnetic spectrum are used for SST estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The first one is regarding the use of climatological SST as the background and subsequently using the same as surface skin temperature along with the latest forecast profiles of temperature and humidity for bias correction in the radiative transfer model. The dominant contribution in the satellite-measured radiances for the split window IR channels comes from the surface skin temperature, as the atmosphere is relatively transparent for window channels [13], except for the weak absorption due to the water vapor. This makes it illogical to use climatological SST at a location along with the forecast atmospheric conditions to represent the simulated satellite radiance or brightness temperature (BT) and subsequently using it for bias correction of actual satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

Thapliyal

2020

Remote Sensing

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Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.

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“…Skin sea surface temperature (SST skin ) is one of the critical variables in the climate system, indicating air-sea interaction patterns near the upper ocean skin layer [1]. The infrared radiometers on earth observation satellites, in both geostationary and polar orbits, have provided retrievals of sea surface temperature (SST) for a half-century [2]. Our choice of satellite radiometers for this analysis was guided by the desire to include one on a polar-orbiting satellite of recent design but with a long planned deployment sequence, a new radiometer type on geostationary satellites again with a long planned deployment duration, and an older radiometer design in geostationary orbit of a type that has been producing data for many years.…”

Section: Introductionmentioning

confidence: 99%

“…The measurements are usually taken in spectral regions with wavelengths from ~3.5 μm to ~4.1 μm and ~10 μm to ~13 μm, where the atmosphere is quite transparent, with variations in clear-sky transmission caused primarily by water vapor, which in itself is highly variable. The widely used SSTskin retrieval algorithm, the non-linear SST (NLSST; [27]), is based on the atmospheric transmission window near the IR112 and IR123 bands (Table 1), with other dependences on satellite zenith angle, first-guess SST, coefficient set for latitude bands and month of year [2,28]. The IR038 band near the 3.7-3.9 μm interval can be used to retrieve nighttime SSTskin and correct dust aerosol effect [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers

Luo

Minnett

2020

Remote Sensing

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The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin sea surface temperature (SSTskin). The successful application of SLSTR-derived SSTskin fields depends on their accuracies. Based on sensor-dependent radiative transfer model simulations, geostationary Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imagers (ABI) and Meteosat Second Generation (MSG-4) Spinning Enhanced Visible and Infrared Imager (SEVIRI) brightness temperatures (BT) have been transformed to SLSTR equivalents to permit comparisons at the pixel level in three ocean regions. The results show the averaged BT differences are on the order of 0.1 K and the existence of small biases between them are likely due to the uncertainties in cloud masking, satellite view angle, solar azimuth angle, and reflected solar light. This study demonstrates the feasibility of combining SSTskin retrievals from SLSTR with those of ABI and SEVIRI.

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Half a century of satellite remote sensing of sea-surface temperature

Cited by 219 publications

References 357 publications

New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations

New Evidence of Mediterranean Climate Change and Variability from Sea Surface Temperature Observations

Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager

Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers

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