An application of satellite-derived sea surface temperature data to the skipjack (Katsuwonus pelamis Linnaeus, 1758) and albacore tuna (Thunnus alalunga Bonaterre, 1788) fisheries in the north-east Atlantic

In April-December 1989, June 1990 and July-October (1990-92), AVHRR/NOAA scenes from the north-east Atlantic (10-50° N and 0-30° W) and Mediterranean Alboran Sea were processed. The objective was to study the relationships between the upper ocean dynamics (synoptic and mesoscale fronts and vorticity) with skipjack tuna (Katsuwonus pelamis Linnaeus, 1758) and albacore tuna (Thunnus alalunga Bonaterre, 1788) fishing ground locations by satellite-derived sea surface temperature (SST) maps and in situ data sets. Results show an analogy between the bioecological responses of both species to the large (SST seasonal drifts) and small-scale oceanographic events (eddy fields, as a consequence of their similar physiological response to all the anomalies at sea.     

Influence of SST on catches of swordfish and tuna in the Portuguese domestic longline fishery

Catch‐and‐effort data from a longline vessel operating off the west coast of Portugal, and thermal imagery from NOAA satellites were employed to investigate the aggregation and availability of swordfish, bigeye and albacore tuna in relation to transient coastal upwelling features. We found a decreasing trend in swordfish catch per unit effort (CPUE) and an increasing trend in tuna species CPUE during the study period in apparent association with the progressively increasing intensity of the coastal upwelling regime from year to year. Swordfish CPUE were significantly higher on the warm side of surface thermal fronts associated with events of intensification/relaxation of coastal upwelling. The fishing efficiency for tuna species was significantly higher during peak upwelling conditions, in the close vicinity of mushroom‐like structures at the edge of upwelling filaments. The present results do not support the existence of a preferred sea surface temperature (SST) range for these fish species. It is suggested that several mechanisms leading to the local concentration of prey provide adequate explanations for the aggregation of these species in the vicinity of frontal structures associated with coastal upwelling processes.     

Deriving the operational nonlinear multichannel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3

The National Oceanic and Atmospheric Administration (NOAA) currently uses Nonlinear Sea Surface Temperature (NLSST) algorithms to estimate sea surface temperature (SST) from NOAA satellite Advanced Very High Resolution Radiometer (AVHRR) data. In this study, we created a three-month dataset of global sea surface temperature derived from NOAA-15 AVHRR data paired with coincident SST measurements from buoys (i.e. called the SST matchup dataset) between October and December 1998. The satellite sensor SST and buoy SST pairs were included in the dataset if they were coincident within 25 km and 4 hours. A regression analysis of the data in this matchup dataset was used to derive the coefficients for the operational NLSST equations applicable to NOAA-15 AVHRR sensor data. An independent matchup dataset (between January and March 1999) was also used to assess the accuracy of these day and night operational NLSST algorithms. The bias was found to be 0.14°C and 0.08°C for the day and night algorithms, respectively. The standard deviation was 0.5°C or less.     

The satellite-measured sea surface temperature change in the Gulf of Finland

Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year^–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.     

Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean

The empirical habitat suitability index (HSI) has been widely used to examine the habitat characteristics of terrestrial animals, though rarely used in highly migratory fish such as tuna. This study used the geographic information system technique to establish empirical models of HSI for yellowfin tuna (YFT) in the Western and Central Pacific Ocean (WCPO). Daily catch data from the Taiwanese purse seine fishery during 2003–2007 were aggregated monthly into sequential degrees before match processing the conducted data to obtain monthly remote-sensing data for multi-environmental factors, including sea surface temperature (SST), chlorophyll-a (chl-a), sea surface height (SSH) and sea surface salinity (SSS). According to the frequency distribution of each factor on which YFT were caught, this study transformed the values of the four factors into a suitability index (SI) ranging from low to high (0–1). These SI values were consequently combined into different empirical HSI models, and the optimum models were selected using the general linear model. The optimum empirical HSI for YFT in the study area was converted for SI (SST, SSH, chl-a and SSS) using the arithmetic mean model, of which the correct prediction rate was 71.9%. An agreement was present between the average HSI and total YFT catch. Furthermore, the high HSI area corresponds with the displacement of catch per unit effort (CPUE).     

Locating tuna forage ground through satellite remote sensing

A technique has been developed to assess tuna habitat using satellite derived ocean colour, water transparency and sea surface temperature. The scientific approach is based on the food and feeding habit and the preferential temperature range of tuna. Time series chlorophyll-a images of Indian Remote Sensing Satellite P4-Ocean Colour Monitor (IRS P4-OCM) have been analysed to define the critical phytoplankton patch size, its persistence and the optimal water transparency depth. Sea surface temperature (SST) data derived from National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) have been analysed to study the optimum temperature range in the surrounding waters. Results based on hindcasting indicate that the tuna forage ground derived from satellite data yielded high catch of tuna (>2% hooking rate). Study reveals that a minimum time delay of 5–7 days is required for a phytoplankton patch to mature to the forage ground. In addition, water transparency for facilitating sight feeding should be about 25–30 m depth. The thermal limit and preferential temperature range observed is 26–30°C and 26–28°C, respectively. Preliminary results based on limited validation are highly promising. However, an experimental forecast is being planned to validate this approach.     

The relationship between multi-sensor satellite data and Bayesian estimates for skipjack tuna catches in the South Brazil Bight

In this study we tested a Bayesian model based on a conjugate gamma/Poisson pair associated with environmental variables derived from satellite data such as sea surface temperature (SST) and its derived gradient fields from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra, chlorophyll-a concentration from Sea Viewing Wide field of View Sensor (SEAWiFS)/SeaStar and surface winds and Ekman pumping from SeaWinds/Quick Scatterometer (QuikSCAT) to predict weekly catch estimates of the skipjack tuna in the South Brazil Bight. This was achieved by confronting the fishery data with model estimates and regressing the results on the satellite data. The fishery data were expressed by an index of catch per unit effort (CPUE) calculated as the weight of fish caught (in tonnes) by fishing week, and were divided into two series, called historical series (1996–1998; 2001), and validation year (2002). The output of model CPUE estimates is in good agreement with the historical weekly CPUE and generated updated weekly estimates that explained up to 62% of weekly CPUE from 2002. In general, the best proxy for the Bayesian weekly estimates is the gradient zonal SST field. The results refined previous knowledge of the influence of SST on the occurrence of skipjack tuna.     

Spatio-temporal distribution of skipjack in relation to oceanographic conditions in the west-central Pacific Ocean

The skipjack tuna, Katsuwonus pelamis, is an economically important oceanic species widely distributed in the west-central Pacific Ocean (WCPO). The spatio-temporal distribution of Katsuwonus pelamis with respect to oceanographic and climatic variables during 1995–2010 in the west-central Pacific was examined in this study using purse seine fishery data from South Pacific Fisheries Commission (SPC). ‘Gravitational centre’ of two temporal scales (i.e. monthly and yearly) of catch per unit effort (CPUE) was calculated to represent the variability of local stock abundance on fishing grounds. Significant inter-annual and seasonal variabilities were observed. Monthly longitudinal ‘centres of gravity’ were correlated with sea surface temperature anomaly (SSTA) in Niño 3.4 region and monthly latitudinal ‘centres of gravity’ reflect a ‘South–North’ migration pattern of Katsuwonus pelamis. The distribution–habitat associations were quantitatively evaluated including SST between 28–30°C, sea surface height (SSH)in the range 90–100 cm, gradient SST between 0.1 and 0.7°C 10 km^?1,and chlorophyll-a(chl-a) between 0.1 and 0.6 mg m^?3 by an empirical cumulative distribution function (ECDF). Four clusters of yearly ‘gravitational centres’ were classified using the k-means method, which could be defined as warmpool fishing ground (WFG) and cold-tongue fishing ground (CFG) according to their oceanographic habitat. The integrated environmental distribution map combined with the developed model (R² = 0.28, p < 0.0001) provides an approach for predicting hotspots of Katsuwonus pelamis. This study improves our understanding of the spatio-temporal dynamics of skipjack tuna, which is critical for sustainable management of this important fisheries resources.     

Mapping land-cover over large areas using multispectral data derived from the NOAA-AVHRR: A case study of Nigeria

Land-cover information for Nigeria was obtained from a countrywide, low-level aerial survey conducted in 1990. A range of spectral vegetation indices (SVIs) and ground surface temperature estimates were calculated for Nigeria using daily data throughout 1990 from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. A supervised classification of the land-cover classes was then performed using a modified discriminant analysis in which predictor variables were selected from the mean, maximum, minimum and standard deviation of the raw waveband AVHRR data, AVHRR derived products and a digital elevation model (DEM). With a 60 per cent threshold coverage by any one of eight major vegetation types the analysis correctly predicted land-cover type with producer accuracies (excluding 'bare ground' with only a few points) of between 48 per cent (cultivation) and 100 per cent (mangrove) (average 74.5 per cent).     

Geometric correction and radiometric normalisation of NOAA AVHRR data for fisheries applications

Sea surface temperature (SST) has been found useful for locating potential fishing grounds (PFGs). Thermal data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) was acquired using a microcomputer (PC/AT 486) based Direct Reception and Processing Terminal (DRPT) developed in-house to map surface thermal fronts. Geometric correction using orbital ephemeris and ground control points (GCPs) resulted in locational accuracy of 1·73 km by 2·1 km. Besides, the corrections for artificially lowering SST in case of passes with large satellite zenith angles (LSZA) through a radiance normalization based on the mean vectors and dispersion matrices to make it comparable with small satellite zenith angle (SSZA) pass is presented in this letter.     

Relation between meteorological conditions and the catching of red tuna (Thunnus thynnus) from the measurements of the TOVS and AVHRR sensors of the NOAA satellites

During the second half of the month of June 1997, a massive catch of red tuna (Thunnus thynnus) took place off the coast of Babarte (Spain), in contrast to the first half of that month when there was hardly any presence of this species.

The aim of this paper was to examine the relation between the high fishing productivity and the meteorological conditions under which the oceanic events to which the tuna fisheries were attracted took place. This was carried out through the analysis of Advanced Very High Resolution Radiometer (AVHRR) sensor data and the data from the Tiros‐N Operational Vertical Sounder (TOVS) probe of the NOAA‐14 satellite from 10 to 24 June 1997.

Results show that the formation of the fishing front was caused by an ocean–atmosphere energetic exchange, which was localized and described through the data transmitted from the NOAA satellites.     

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.     

Environmental effects on yellowfin tuna catch by the Taiwan longline fishery in the Arabian Sea

In this study, we collected environmental variables to investigate their effects on the catch per unit effort (CPUE) of yellowfin tuna in the Arabian Sea during the period 1980–2005. We used an advanced time series analysis, including a state-space approach to remove seasonality, and wavelet analysis to investigate transient relationships. For large-scale environmental effects, we used the dipole mode index (DMI) to represent the Indian Ocean dipole; for local environmental factors, we investigated sea surface temperature (SST), thermocline depth and chlorophyll-a (chl-a) concentration. The main factors causing interannual variations in the CPUE might change with time. CPUE showed positive correlations with SST and DMI from the beginning of the 1980s to the middle of the 1990s. It also showed a significant coherence with chl-a, especially a long-term positive correlation for the regular longline fishery in 1998–2005 with a periodicity of 2 years. Both regular and deep longline CPUEs were found to have significant coherence with thermocline depth having a periodicity of 3 years. The relations were of opposite signs such that the shallow thermocline depth produced a high CPUE for the regular longline fishery and deep thermocline depth caused a high CPUE for the deep longline fishery.     

Seasonal variation of sea surface temperature in the Bay of Bengal during 1992 as derived from NOAA-AVHRR SST data

Monthly maps of sea surface temperature (SST) derived from NOAA (National Oceanic and Atmospheric Administration)-AVHRR (Advanced Very High Resolution Radiometer) data during 1992 for the Bay of Bengal are analysed and compared with the available/compiled monthly seatruth (bucket thermometer) data of this region. It was noticed that the computed SST bias (AVHRR SST minus Seatruth SST), in general, varied between 2.0 and 2.5 C with smaller bias values (1.5 to 1.5 C) during January-June and December. Larger bias values were noticed in the south-eastern Bay in July and in the Andaman Sea in October. The large SST biases suggested the necessity for improvement of SST algorithms by properly removing the clouds. The spatial variation of Standard Deviation of SST bias was particularly high (0.7) in the western Bay when compared to other parts of the Bay of Bengal. The monthly maps of AVHRR SST clearly depicted the seasonal cycle of SST showing the well known bi-modal SST distribution of the study region with winter cooling, summer heating, monsoonal cooling and post-monsoon warming phases. The seasonal cycle of SST further revealed the persistence of Warm Pool (SST 28 C) in the Bay of Bengal from March through October.     

Synergistic analysis of SeaWiFS chlorophyll concentration and NOAA-AVHRR SST features for exploring marine living resources

Near-synchronous Sea-viewing Wide Field-of-View Sensor (SeaWiFS) derived chlorophyll concentration and National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) images were used to understand patterns, persistence and inter-relationship between ocean colour and thermal features. Different types of oceanic features were observed on chlorophyll concentration and SST images. An inverse relationship between chlorophyll concentration and SST features was observed. The features observed in the chlorophyll concentration images were well defined and appeared to contained more information than those on the SST images. The frontal zone positions on the chlorophyll concentration images coincided with temperature boundaries at some locations. This coincidence indicates that the physical and bio-chemical processes are closely coupled at these locations. High fish catch points were found in the vicinity of these features. A synergistic analysis of chlorophyll concentration and SST may increase our understanding of the inter-relationship between environmental variables for locating potential fishing grounds.     

AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China

The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10×10⁵ m³ year⁻¹ of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1×1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 °C. During the summer and fall months (May to November), there is a larger thermal plume extending 8-10 km south along the coast from DNPS, and the temperature change is about 1.0 °C. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume.     

Pre‐monsoon Indian Ocean SST in contrasting years of Indian summer monsoon rainfall

The anomalous change in SST of June with reference to May studied for the Indian Ocean region (0–120°E, 40°S–40°N) during 1998 to 2005. The change in monthly SST anomaly in the equatorial region were studied along with changes in water vapor and wind field in 1998 and 2002, the years that representing contrasting changes in the summer monsoon rainfall. The westward extending equatorial warm pool in the Indian Ocean was found relative weak and found relatively weak during 1998 in contrast to those in 2002. Similar analysis further extended till 2005 indicated further the influence of the equatorial warm pool on the summer monsoon rainfall over the Indian subcontinent.     

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.     

Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data

This research explores the relationship between El Nino/Southern Oscillation (ENSO), captured by equatorial Pacific Ocean Sea Surface Temperature (SST), and interannual variation in vegetation vigour in the southeast USA, captured by Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI), for the period 1982-1992. The moving average and 'baseline' methods (anomaly from the long term mean) were used to extract interannual patterns in the NDVI signature for croplands, deciduous forests and evergreen forests. The ENSO cycle was measured using mean SST anomalies and the percentage of SST cells above certain threshold values (e.g. 1.0° C above the long term mean). The baseline method indicated a weak, yet persistent, negative correlation between ENSO warm phase events and vegetation vigour in the south-east USA. The moving average method yielded similar results but produced higher correlation values (-0.45 to-0.76, significant at the 0.01 level). Use of the 2.0° C threshold SST anomaly was found to yield the highest correlation values as it captures not only the presence but also the intensity of ENSO warm phase events. These results indicate that there is a clear and recognizable, though inconsistent, relationship between ENSO and vegetation vigour in the south-east USA.