Multichannel Sea Surface Temperature Research Articles

Retrieval of Sea Surface Skin Temperature from the High Resolution Picture Transmission Data of the National Oceanic and Atmospheric Administration Series Satellites

The High Resolution Picture Transmission (HRPT) data of the National Oceanic and Atmospheric Administration (NOAA) series meteorological satellites had been received by the SeaSpace ground station located at the Ocean University of China (OUC). Based on the atmospheric radiative transfer model, we obtained the NOAA-15/16/17/18/19 Advanced Very High Resolution Radiometer (AVHRR) sea surface skin temperature (SSTskin) data using the Bayesian cloud detection method and the optimal estimation (OE) sea surface temperature (SST) retrieval algorithm. Compared with the NOAA/AVHRR multi-channel SST data, the AVHRR SSTskin data have higher data accuracy. We also compared the AVHRR SSTskin with the buoy SST with spatial and temporal windows of 0.01° and 30 min. The daytime biases ranged from −0.32 °C (NOAA-16) to 0.08 °C (NOAA-17) with standard deviations (SDs) ranging from 0.36 °C (NOAA-18/ NOAA-19) to 0.60 °C (NOAA-16), and the nighttime biases ranged from −0.26 °C (NOAA-16) to −0.02 °C (NOAA-17) with SDs ranging from 0.33 °C (NOAA-19) to 0.60 °C (NOAA-16). The accuracy of all five satellite data during daytime and nighttime was significantly improved. These results show that the AVHRR SSTskin of NOAA series satellites is good and consistent in different periods, and the SSTskin data products with high spatial resolution and accuracy can be used for mesoscale and submesoscale marine applications.

Fishing Ground Mapping Model in The Semi-Enclosed Saleh Bay, West Nusa Tenggara

Saleh Bay is a semi-enclosed area of water in Nusa Tenggara Barat Province that is enriched by fisheries resources. The bay’s strategic position, surrounded by several small islands, makes it an area of fertile water. An area of water is considered a potentially ideal fishing ground if it contains several oceanographic phenomena, including thermal fronts and upwelling. Fishing activities in Saleh Bay have been found to be ineffective and inefficient due to local people’s continued use of traditional methods such as fishing by signs of nature (instincts), wind direction, astrological signs and previous experience. This study aimed to create a mapping model of the fishing grounds in Saleh Bay based on remote sensing satellite data. Spatial analysis of daily level 3 images from the Suomi-National Polar-Orbiting Partnership (SNPP) was conducted throughout January and August 2019. The image acquisition period was adapted based on the seasonal system of Indonesia. The study area was determined based on thermal front events as identified by sea surface temperature (SST) data analysed using statistical regression with a Non-Linear Multi-Channel SST (NLSST) approach. An ideal fishing ground is characterised by several oceanographic settings such as upwelling and thermal front occurrence. The average SST distribution in January 2019 was relatively high, ranging from 30.39 to 33.70 oC, while in August 2019, the temperature declined significantly, ranging from 25.09 to 29.30 oC. Concerning the fishing ground model, a plethora of potential fishing ground areas were identified in August compared to January 2019, at 144 and 42 points respectively. This reflected the density of the fishing grounds observed. The fishing grounds were most likely to be concentrated in the bay mouth during the southwest monsoon and within the bay near the plateau during the northeast monsoon. The seasonal variability of Saleh Bay played a significant role in the spatial extraction of the fishing ground data.

Sea Surface Temperatures Derived From COCTS Onboard the HY-1C Satellite

As one of the key payloads onboard the HY-1C satellite, the Chinese ocean color and temperature scanner (COCTS) was designed for global sea surface temperature (SST) and ocean color detection. In this study, a nonlinear SST algorithm (NLSST) is applied to derive SSTs from the brightness temperatures (BTs) of the two COCTS thermal infrared bands at 10.8 and 12.0 μm. The SST retrieval results from the multichannel SST algorithm (MCSST) are used as the first guess for the input of the NLSST algorithm. The coefficients of both the MCSST and NLSST algorithms are regressed from the matchup datasets of the COCTS BT observations and the measurements from the in situ quality monitor system (iQuam), which were collected from September 10 to December 31, 2018. The retrieval SSTs from January 1, 2019 to March 31, 2020 are evaluated by the in situ measurements from iQuam with root mean square errors of 0.84 °C for daytime and 0.97 °C for nighttime and robust standard deviations (RSDs) of 0.73 °C for daytime and 0.72 °C for nighttime. Daily gridded retrieval SSTs from September 10, 2018 to March 31, 2020 are compared with SSTs from moderate-resolution imaging spectroradiometer (MODIS) onboard Terra satellite. The cross comparison RSDs are 0.56 ± 0.05 °C for daytime and 0.67 ± 0.05 °C for nighttime.

Causas y consecuencias de las lluvias extraordinarias de 2017 en la costa ecuatoriana: el caso de la provincia Manabí

Extraordinary rainfall events are the object of research in different parts of the world, where their occurrence has caused negative socioenvironmental impacts, expressed in substantial human and material losses. The objective of this work is to analyze the causes and consequences of the extraordinary rains registered in Manabí in February and March 2017, emphasizing its social, economic andenvironmental effects. It is assumed that these events are closely related to the anomalies of sea surface temperature in the equatorial Pacific Ocean. Field research was conducted by analyzing sea surface temperature (SST) data in satellite imagery using the Split Night Multi Channel Sea Surface Temperature algorithm to identify thermal anomalies. With the precipitation records of meteorological stations of the Ecuadorian coastal region, a descriptive statistical analysis was conducted to determine the annual behavior (regime) and historical rainfall. Socioeconomic impacts were studied from unstructured interviews with key informants, through non-probability sampling, as well as primary information from government agencies.The results indicate that the extraordinary rains observed during the February-March 2017 period were a consequence of the combination of factors such as positive thermal anomalies in the equatorial Pacific, intertropical convergence and atmospheric instability in the Amazon. This generated negative impacts in urban and rural localities such as Manta, Portoviejo, Lodana, Calceta and Chone.

High-Resolution Sea Surface Temperature Retrieval from Landsat 8 OLI/TIRS Data at Coastal Regions

High-resolution sea surface temperature (SST) images are essential to study the highly variable small-scale oceanic phenomena in a coastal region. Most previous SST algorithms are focused on the low or medium resolution SST from the near polar orbiting or geostationary satellites. The Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) makes it possible to obtain high-resolution SST images of coastal regions. This study performed a matchup procedure between 276 Landsat 8 images and in-situ temperature measurements of buoys off the coast of the Korean Peninsula from April 2013 to August 2017. Using the matchup database, we investigated SST errors for each formulation of the Multi-Channel SST (MCSST) and the Non-Linear SST (NLSST) by considering the satellite zenith angle (SZA) and the first-guess SST. The retrieved SST equations showed a root-mean-square error (RMSE) from 0.59 to 0.72 °C. The smallest errors were found for the NLSST equation that considers the SZA and uses the first-guess SST, compared with the MCSST equations. The SST errors showed characteristic dependences on the atmospheric water vapor, the SZA, and the wind speed. In spite of the narrow swath width of the Landsat 8, the effect of the SZA on the errors was estimated to be significant and considerable for all the formations. Although the coefficients were calculated in the coastal regions around the Korean Peninsula, these coefficients are expected to be feasible for SST retrieval applied to any other parts of the global ocean. This study also addressed the need for high-resolution coastal SST, by emphasizing the usefulness of the high-resolution Landsat 8 OLI/TIRS data for monitoring the small-scale oceanic phenomena in coastal regions.

The application of a hybrid sea surface temperature algorithm to COMS geostationary satellite data in the Northwest Pacific Ocean

ABSTRACT The hybrid sea surface temperature (SST) algorithm derives coefficients for SST estimation based on the regression procedure of incremental values between the satellite-observed brightness temperature (BT) minus the first-guess BT and the in-situ temperature minus the first-guess SST. In this study, the hybrid SST algorithm was applied to retrieve the SST coefficients of which the results were compared with extensively used regression algorithms of Multi-Channel SST (MCSST) and Non-Linear SST (NLSST) using data obtained by the Meteorological Imager of the first Korean geostationary satellite, the Communication, Ocean and Meteorological Satellite, in the Northwest Pacific Ocean in May 2014. The empirical regression SSTs and hybrid SSTs exhibited similar root-mean-squared errors in the range of 0.61–0.84°C. Although the SST errors were quite similar to each other, the comparison of the hybrid SSTs with the in-situ SSTs indicated a slightly higher accuracy of approximately 0.2°C compared with that of the empirically derived SSTs. SST retrieval using the hybrid algorithm reduced the errors near the cloud edges. The differences between the hybrid SST and regression SST tended to be greater when the distance to the nearest cloudy pixel was less than 0.2°. This study discusses the potential causes of the differences between the SST errors of the algorithms and addresses a few constraints in the execution of the hybrid algorithm related to the numerical model.

On the use of NLSST and MCSST for the study of spatio-temporal trends in SST gradients

ABSTRACTOngoing efforts are dedicated by meteorological and oceanographic agencies to improve the accuracy of Sea Surface Temperature (SST) estimates from satellite observations via improved retrieval algorithms and validation data. An important application of satellite-based SST observations is the analysis of the spatio-temporal characteristics of ocean fronts, which depend on several parameters including the SST retrieval scheme from Top-of-Atmosphere Brightness Temperatures. In this study, we focus on two widely used SST retrieval algorithms, namely the Multichannel SST (MCSST) and the Nonlinear SST (NLSST). Using night-time Level 2 SST derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua from 2005 to 2015 over the Southwestern Atlantic Ocean (SAO), we show that 1) the spatial distribution and temporal variability of SST gradient magnitudes derived from these two SST retrieval schemes are different despite statistical consistency of SST fields 2) the widely used NLSST formulation introduces a correlation between SST gradient magnitudes and SST values. This correlation, likely due to the use of a first guess SST in the NLSST formulation, is not observed in the MCSST data and may affect the study of long-term changes in ocean dynamics.

Sea Surface Temperature Retrieval from the First Korean Geostationary Satellite COMS Data: Validation and Error Assessment

Korea’s first geostationary satellite, the “Communication, Ocean, and Meteorological Satellite” (COMS), has been operating since 2010. The Meteorological Imager (MI), an sensor on-board the COMS, has observed sea-surface radiances for the estimation of sea surface temperature (SST) in the western Pacific Ocean and eastern Indian Ocean. To derive the SST coefficients of COMS, quality-controlled surface drifting buoy data were collected for the period of April 2011 to March 2015. A collocation procedure between COMS/MI data and the surface drifter data produced a matchup database for 4 years from 2011 to 2015. The coefficients for the COMS/MI SST were derived by applying appropriate algorithms, i.e., the Multi-channel SST (MCSST) and Non-linear SST (NLSST) algorithms, for daytime and nighttime data using a regression method. Validation results suggest the possibility of the continuous use of the coefficients as representative SST coefficients of COMS. The estimated SSTs near the edge of a full disk with high satellite zenith angles over 60° revealed relatively large errors compared to drifter temperatures. Most of NLSST formulations exhibited overestimation of SSTs at low SSTs (<10 °C). This study suggests an approach by which SST can be measured accurately in order to contribute to tracking climate change.

ANALISIS PENGGUNAAN PERSAMAAN MULTICHANNEL SEA SURFACE TEMPERATUR (MCSTT) SPLIT-WINDOW PADA SENSOR SATELIT NOAA-AVHRR UNTUK DETEKSI TEMPERATUR PERMUKAAN AIR LAUT

<em> Sea surface temperatures have a great influence on weather conditions and terrestrial climates. The phenomena that occur in the oceans such as La Nina and El Nino also have a great impact on the changing world weather. For that required data of sea surface temperature up to date. The remote sensing technology can be used to monitor up-to-date seawater temperatures using NOAA's AVHRR-rated NOAA radios that have three thermal infrared channels, namely channel 3 (3.33-3.93μm), channel 4 (10.3 -11,3μm), and channel 5 (11,5 - 12μm) and by using Split-Window Multichannel Sea Surface Temperature (MCSTT) to calculate sea surface temperature. In this research will be an analysis of the accuracy of the use of these equations for tropical perariran in Indonesia. Field verification performed on the coast in Tuban region with geographical coordinates (6.83099<sup>o</sup>- 6.76149<sup>o</sup> SL and 112,029<sup>o</sup>-112,101<sup>o</sup>EL) by measuring the temperature at 30 points for comparison. The results of these measurements are then tested statistics Kolmogorov-Smirnov test and the results of temperature data obtained from the calculation and measurement of both normal distribution then because the normal distribution is done t test with 95% confidence level to compare between the temperature obtained from the calculation by using the value gray pixels with temperature measurements in the field turned out the average population is not the same or different significantly and the difference between the temperature shown image with temperature measurement results of 0.9886 <sup>o</sup>C</em>

A bias correction method for FY-3C VIRR SST data

ABSTRACTA bias correction method was proposed for correcting the significant negative sea surface temperature (SST) biases in Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR) products. The multichannel SST algorithm (MCSST) for the daytime SST and night-time SST of VIRR products are modified for estimating the debiased VIRR SSTs, and the optimal local matchup data are selected for each SST observations based on the distance to each parts of regressors in R-space. Compared with the probability density function matching technique for bias correction, the proposed method can better remove the negative biases and decrease the standard deviations of biases.

Influence of water-temperature variability on stony coral diversity in Florida Keys patch reefs

Satellite-derived sea surface temperature (SST) The Advanced Very High Resolution Radiometer (AVHRR) imagery obtained from NOAA polar orbiting satellites (i.e., NOAA-12, 14, 16, and 19) were collected by the High Resolution Picture Transmission antenna, at the Institute for Marine Remote Sensing (IMaRS) at the University of South Florida. SST was calculated using the multichannel sea surface temperature algorithm developed by McClain et al. (1983) and updated calibration coefficients for each satellite sensor in orbit. Satellite imagery was mapped to a cylindrical equidistant projection and cloud filtered (Hu et al. 2009).

NOAA/AVHRR sea surface temperature accuracy in the East/Japan Sea

Sea surface temperature (SST) retrieved from Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting environmental satellites were validated in the East/Japan Sea (EJS) using surface drifter measurements as ground truths from 2005 to 2010. Overall, the root-mean-square (rms) errors of multichannel SSTs (MCSSTs) and non-linear SSTs (NLSSTs) using global SST coefficients were approximately 0.85°C and 0.80°C, respectively. An analysis of the SST errors (satellite – drifter) revealed a dependence on the amount of atmospheric moisture. In addition, satellite-derived SSTs tended to be related to wind speeds, particularly during the night. The SST errors also demonstrated diurnal variations with relatively higher rms from 0.80°C to 1.00°C during the night than the day, with a small rms of about 0.50°C. Bias also exhibited reasonable diurnal differences, showing small biases during the daytime. Although a satellite zenith angle has been considered in the global SST coefficients, its effect on the SST errors still remained in case of the EJS. Given the diverse use of SST data, the continuous validation and understanding of the characteristic errors of satellite SSTs should be conducted based on extensive in-situ temperature measurements in the global ocean as well as local seas.

Characteristics of sea surface temperature retrieved from MTSAT-1R and in-situ data

Multi-channel sea surface temperature (MCSST) data were retrieved from the Japanese geostationary satellite, MTSAT-1R, for East Asia in western North Pacific. The coefficients used to calculate the MCSST data were estimated by assuming a linear relationship between the brightness temperatures obtained from the satellite and the in-situ buoy SST. It is important to remove cloud contamination pixels to retrieve meaningful information from infrared data. Therefore, the cloud detection algorithm was improved by using a 10-day maximum or minimum composite map for infrared and visible channels. The RMSE of the MCSST in comparison with the two-year buoy SST was about 0.89oC. The error was the largest at mid-latitudes in summer. Additionally, the error between the two SSTs showed that diurnal variation had a positive bias during daytime and a negative bias during nighttime. Furthermore, in 2007, both SSTs showed seasonal and spatial diurnal variation. The magnitude of the daily variation in the MCSST was two times larger than that in the buoy SST, and this was attributed to diurnal heating with a weak surface wind speed.

Validation of JAXA/MODIS Sea Surface Temperature in Water around Taiwan Using the Terra and Aqua Satellites

The research vessel-based Conductivity Temperature Depth profiler (CTD) provides underwater measurements of the bulk sea surface temperature (SST) at the depths of shallower than 5 m. The CTD observations of the seas around Taiwan provide useful data for comparison with SST of MODIS (Moderate Resolution Imaging Spectroradiometers) aboard Aqua and Terra satellites archived by JAXA (Japan Aerospace Exploration Agency). We produce a high-resolution (1 km) MODIS SST by using Multi-Channel SST (MCSST) algorithm. There were 1516 cloud-free match-up data pairs of MODIS SST and in situ measurements during the period from 2003 - 2005. The difference of the root mean square error (RMSE) of satellite observations from each platform during the day and at night was: 0.88°C in Aqua daytime, 0.71°C in Aqua nighttime, 0.71°C in Terra daytime, and 0.60°C in Terra nighttime. The total analysis of MODIS-derived SST shows good agreement with a bias of 0.03°C and RMSE of 0.75°C. The analyses indicate that the bias of Aqua daytime was always positive throughout the year and the large RMSE should be attributed to the large positive bias (0.45°C) under diurnal warming. It was also found that the bias of Terra daytime was usually negative with a mean bias of -0.41°C; its large RMSE should be treated with care because of low solar radiation in the morning. Key word: SST, Taiwan, MODIS, Satellite observation

Multi‐scale thermal pattern monitoring of a large lake (Lake Geneva) using a multi‐sensor approach

The applicability of satellite imagery products from different sensors (AVHRR‐derived multi‐channel sea surface temperature (MCSST), MODIS sea surface temperature (SST) products 5‐Min L2 Swath 1 km and Landsat TM band 6 thermal signature) for the comprehensive monitoring of temperature and its temporal patterns over a large lake is tested in this study. The coverage of cloud‐free satellite data for Lake Geneva is reported throughout a year and, more specifically, during a 13 day period in summer 2003. In a second step, we demonstrate the feasibility of the AVHRR/MODIS imagery to discern day and night temperature patterns, by generating day and night climatologies and various spatial statistics over the 13 day period. The different day and night surface thermal patterns observed by satellite imagery could be linked to the thermal structure existing in deeper water using the concept of the diurnal decoupled layer. The forcing of the persistent patterns, two warm cores divided by a saddle‐shaped cold anomaly, is explained by wind periodicity and insolation conditions. The patterns can be matched to features postulated by findings of different limnologists in the past. Other surface temperature related phenomena such as water upwelling and downwelling and the occurrence of plumes are related to meteorological and hydrological events. The lakewide average lake surface water temperature (LSWT) trends for day and night during the study period are roughly parallel. A sudden loss of stored heat can be explained by episodes of long fetch, synoptic wind (bise) that interrupted the predominant breeze regime.

Sea surface temperature observation by Global Imager (GLI)/ADEOS-II: Algorithm and accuracy of the product

A sea surface temperature (SST) retrieval algorithm for Global Imager (GLI) aboard the ADEOS-II satellite has been developed. The algorithm is used to produce the standard SST product in the Japan Aerospace Exploration Agency (JAXA). The algorithm for cloud screening is formed by combinations of various types of tests to detect cloud-contaminated pixels. The combination is changed according to the solar zenith angle, which enables us to detect clouds even in the sun glitter region in daytime. The parameters in the cloud-detection tests have been tuned using the GLI global observations. SST is calculated by the Multi-Channel SST (MCSST) technique from the detected clear pixels. Using drifting buoy measurements, match-up data are produced to derive the coefficients of the MCSST equations and to examine their performance. The bias and RMSE of the GLI SST are 0.03 K and 0.66 K for daytime and, −0.01 K and 0.70 K for nighttime, respectively.

Sea Surface Temperature Daytime Climate Analyses Derived from Aerosol Bias-Corrected Satellite Data

Abstract This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.

Heavy rainfall episodes in Ecuador during El Niño events and associated regional atmospheric circulation and SST patterns

Abstract. To date very little is known about the relation between regional circulation patterns and sea surface temperature development in the Niño 1,2 region and the occurrence of heavy precipitation in Ecuador and northern Peru. The current study uses a comprehensive data set of 2544 Meteosat-3 imagery to investigate the dynamics of heavy precipitation during El Niño in 1991/92. Rainfall maps are retrieved by means of an adjusted version of the Convective Stratiform Technique (CST) and Cloud Motion Winds (CMW) are extracted from image sequences by using a special cross-correlation approach. A spatial factor analysis is applied to extract specific weather situations with heavy precipitation during El Niño events. The factor analysis yielded 16 factors. It has been proven that the factor patterns with the highest variance explanation also occur during the rainy season of non-El Niño years. However, 6 El Niño-specific situations could be derived which cause heavy rainfall, especially in coastal Ecuador and northern Peru. Multi-channel Sea Surface Temperatures (MCSST) and cloud motion winds are used to describe atmospheric and oceanic dynamics for these specific weather situations. The analysis shows that high SSTs in combination with strong SST gradients off the coast and warm SST bubbles lead to regional differences in moist instability and heavy rainfall. Both large scale circulation (reversal of the Walker cell) and regional dynamics (extended land-sea-breeze system) have been proven to contribute to El Niño rainfall.

DMSP/OLS 영상에서 관측한 동해 야간조업어선의 분포 특성

Monthly distributions of fishing boats at night in the East Sea are described, using defense meteorological satellite program (DMSP)/operational linescan system (OLS) images and common squid (Todarodes pacificus) catches data. We also estimated temperature in 50m, which is main catch depth of the squid, by MCSST (multi-channel sea surface temperature). We examined DMSP/OLS, MCSST and other observation data from 1993 to 2000. We assumed that squid were caught in areas where fishing boats were located. Fishing boats at night appeared only near the Korea/Tsushima Strait from January to March. Fishing boats moved to the northward from April to Jun, distribution of fishing boats in spring appeared greater than those in winter. In summer (July-September), center of fishing grounds was formed near the Uleung Island in the south east coast of Korea. The north-south distribution range of fishing boats in October appeared to be greater than that in other months. In particular, we estimated main season of squid catches based on distribution range reflecting the number of fishing boats of north-south and east-west directions from September to December. Relationship between satellite estimate SST and in situ SST showed high correlation (0.91). The correlation between the SST and 50m depth temperature, estimated based on the satellite SST, was relatively high in February, April and October.

Sea surface temperature GOES-8 estimation approach for the Brazilian coast

This work presents a quite consistent procedure for estimation of sea surface temperature (SST) using data from the new generation of the Geostationary Operational Environmental Satellite (GOES). The SST methodology is based on the classical split-window equation. The regional split-window coefficients (A 0, A 1, A 2 and A 3) are estimated by an algorithm regression taking as dependent variable three datasets, i.e. the SST derived from National Oceanic and Atmospheric Administration (NOAA)-14 polar-orbiting satellite and from buoys of Pilot Research Moored Array in Tropical Atlantic (PIRATA) and National Programme of Buoys (PNBOIA). This work shows that the main advantage of the GOES-8 SST algorithm, in comparison with the multi-channel sea surface temperature (MCSST) procedure using Advanced Very High Resolution Radiometer (AVHRR) data, is the high frequency sampling imagery (each half-hour) which permits a daily image with much less quantity of cloud contamination. The algorithm results using AVHRR/NOAA-14 as input dataset for the regression show that the accuracy of the GOES-8 SST algorithm is better than 1.0°C for all Brazilian coast. For regional estimation, the accuracy has been improved to around 0.5°C. Also, the accuracy of GOES-8 SST is better than 0.7°C using in situ SST collected from moored and drifting buoys.