Spatial and temporal variability of satellite-derived sea surface temperature in the Barents Sea

The Barents Sea (BS) is an important region for studying climate change. This sea is located on the main pathway of the heat transported from low to high latitudes. Since oceanic conditions in the BS may influence vast areas of the Arctic Ocean, it is important to continue to monitor this region and analyse the available oceanographic data sets. One of the important quantities that can be used to track climate change is the sea surface temperature (SST). In this study, we have analysed the 32 years, (1982–2013) National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation SST Version 2 data for the BS. Our results indicate that the regionally averaged SST trend in the BS (about 0.03°C year^–1) is greater than the global trend. This trend varies spatially with the lowest values north from 76° N and the highest values (about 0.06°C year^–1) in proximity of Svalbard and in coastal regions near the White Sea. The SST and 2 m air temperature (AT) trends are high in winter months in the open BS region located west from Novaya Zemlya. Such trends can be linked to a significant retreat of sea ice in this area in recent years. In this article, we also documented spatial patterns in the annual cycle of SST in the BS. We have shown that the interannual variability of SST is similar in different regions of the BS and well correlated with the interannual patterns in AT variability.     

An application of satellite-derived sea surface temperature data to the Australian fishing industry in near real-time

Abstract

A four-year project conducted from 1981 to 1985 examined the application of satellite-derived near real-time sea surface temperature data in assisting the Tuna fishing industry located along the southwestern coast of Australia. The satellite imagery employed was obtained from the NOAA series of polar orbiting satellites. Since the southern bluefin tuna is a pelagic species, it had been anticipated that a good correlation would be found between sea surface temperatures and catches. Early experimental results tended to confirm that view, but a number of anomalies existed in those results. It is now suggested that a weak correlation exists between temperature and catches, but this is due to localized factors. A theory justifying this assumption is given.     

Can phytoplankton community structure be inferred from satellite-derived sea surface temperature anomalies calculated relative to nitrate depletion temperatures?

Hydrographic data collected in the upper 50 m off La Jolla, CA, USA (31°N, 117°W) between 1970 and 1972 were reanalyzed to examine temporal variability in the local temperature-nitrate relationship and to document how chlorophyll a concentration and phytoplankton community structure covary with the temperature-nitrate relationship. Based on the linear expression y=mx+b, the y-intercepts (b), slopes (m), and x-intercepts (−b/m or nitrate depletion temperature, NDT) of four seasonal (January-March, April-June, July-September, and October-December) temperature-nitrate relationships, obtained from the combined multiyear data set, were statistically different from each other and varied around overall multiyear values of b=72.73 μM, m=−5.33 μM °C⁻¹, and NDT=13.65 °C. Three interannual temperature-nitrate relationships from February to April 1970, 1971, and 1972 also had y-intercepts, slopes, and x-intercepts that were statistically different from each other. Nevertheless, limited variability in direct comparisons among seasonal or interannual regression lines and a 1 °C La Jolla NDT range compared to a 25 °C global NDT range supported the general utility of NDT-based comparisons. A nitrate-normalized temperature axis (T−NDT) was created for the La Jolla data set by subtracting NDT from the recorded water column temperatures (T). Chlorophyll a reached a maximum between 0 and 2 °C on this T-NDT axis that ranged from −4 to 10 °C. Microscope-based determinations of La Jolla centric diatom, pennate diatom and dinoflagellate abundances, and La Jolla chlorophyll a, partitioned in proportion to the numerical abundance of the three groups, both peaked in logical progression along the T-NDT axis. In a separate analysis of high-performance liquid chromatography (HPLC) data from three Atlantic Meridional Transect (AMT) cruises (50°N to 52°S), chlorophyll a peaked below 0 °C and three different phytoplankton classes, nanoflagellates, large eukaryotes and prokaryotes, distributed in logical progression along a sea surface temperature (SST) minus NDT axis. To further generalize these results, a previously reported 1° latitude×1° longitude grid of NDTs for the world ocean was applied to satellite-derived grids of SST for March 1999 through June 2000. The SST−NDT calculation provided a standard nitrate-normalized axis simultaneously applicable to all locations in the world ocean. Sixteen plots of satellite-derived chlorophyll a versus SST−NDT for March 1999 through June 2000 demonstrated the opposing seasonal movements of northern and southern hemisphere chlorophyll a along the SST-NDT axis. Based on the phytoplankton community patterns along the temperature minus NDT in the La Jolla and AMT data sets, this chlorophyll a movement along the SST-NDT axis can be associated with phytoplankton community changes related to location around SST−NDT=0 °C. The SST−NDT index appears to provide a useful tool for interpreting the character of the phytoplankton community structure contributing to satellite-derived chlorophyll a in the world ocean.     

A comparative assessment of satellite-derived Adriatic Sea surface temperature

In this article, five Advanced Very High Resolution Radiometer (AVHRR) and four Moderate Resolution Imaging Spectrometer (MODIS)-based Adriatic-focused satellite sea surface temperature (SST) products are analysed and compared with two sets of in situ SST measurements: a drifter-based dataset collected in 2003, and a platform-based dataset gathered in 2004; an additional set was used to validate the new SST coefficients. Analysis of satellite minus in situ SST residuals shows similar results for both in situ datasets, with the differences being within 0.2 K. All daytime SST biases exhibited positive values (less than 0.5 K). Night-time biases for short-wave infrared (IR) algorithms exhibited near zero and small negative values with an exceptionally low standard deviation (about 0.3 K) regardless of the sensor used. Analysis of filtered residual time-series allowed direct comparison between different SST products. The seasonal change in the daytime biases was found to covary with similar changes in atmospheric water vapour and the Adriatic specific wind regime.     

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.     

Infrared sensing of sea surface temperature from space

Error sources in infrared remote sensing of sea surface temperature are discussed, e.g., imperfect transmittance models, uncertain or unknown atmospheric pressure-temperature-humidity vertical profiles, temperature discontinuities at the air-sea interface, temperature differences between surface and bulk water, and neglect of surface emissivity and reflectance. Some of these are analyzed using a simplified version of the transmittance function of Prabhakara et al. (1974). The rms error in conventional sea surface temperature retrievals, in which computers are used to integrate the equation of radiative transfer over many atmospheric layers, has thus far been reduced to about ±1 K (Maul, 1980). This error is for optimum conditions, and seems irreducible. Unless the accuracy can be improved it seems impractical to spend so much effort on lengthy computer retrievals. Prabhakara et al. (1974) have devised a much simpler retrieval method using three infrared bands, which yields an rms error of ±1.1 K. A very simple method yielding ±1.0 K with two infrared bands is described here.     

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.     

Using sea surface temperature measurements from microwave and infrared satellite measurements

The Tropical Rainfall Mapping Mission Microwave Imager (TMI) instrument Sea Surface Temperature (SST) product (v1.0) is compared with in situ observations obtained in the Atlantic Ocean. The TMI SST has a mean warm bias of 0.25?K±0.7?K when compared to in situ SST at a depth of 7?m. When TMI SST are compared to in situ skin SST measurements, the bias is 0.6?K±0.5?K. A limited global comparison between TMI SST and co-incident ERS-2 Along-Track Scanning Radiometer (ATSR/2) skin SST demonstrates a bias of 0.6?K±0.6?K consistent with the result obtained using in situ observations. These results are consistent with the predicted accuracy of the TMI SST data products. Based on these results, a simple method to merge the TMI and ATSR data is proposed.     

海面温度栅格图的锋面提取与矢量化

提出一种海面温度栅格图的锋面提取方法。针对海洋表层温度(SST)锋面强度分布不均匀的特点,利用低通滤波对表温梯度图像进行平滑。再利用迭代法确定出梯度图像的分割阈值,将图像分割成目标与背景两部分。通过数学形态学中图像细化的方法,提取海洋温度锋面的骨架并对细小分枝进行修剪。经矢量化生成锋面线后,利用抹角法对折线进行光滑。最后以西太平洋为例,给出了一个表温锋面提取的实例,表明利用此方法进行海表温度锋面的提取是可行与有效的。     

A non-regression-coefficients method of sea surface temperature retrieval from space

For several years now NOAA/NESDIS have derived an operational global sea surface temperature (SST) product from the AVHRR instrument on the NOAA satellites. This is done using the MCSST and CPSST algorithms which contain coefficients that are determined from a regression analysis of satellite data against in situ surface data. The current algorithms are used to provide global SST data without taking into account the latitude, climate or location of the satellite data, although the CPSST coefficients do have a weak dependence on the satellite brightness temperatures. Because of this global application the current SST algorithms have inherent errors due to local climate influences. In this paper a new SST algorithm is developed that does not rely on regression analysis to derive its coefficients. By using the spatial variation of the brightness temperatures in a small area (50 km by 50 km) it is possible to derive the appropriate coefficients to use in the algorithm. The SST field can thus be derived at any location without need for prior determination of the algorithm coefficients. In a simulation study, data from twenty-five radiosonde ascents-arc use with an atmospheric transmission model to derive a range of atmospheric transmittances and satellite brightness temperatures. Coincident AVHRR data and ship data are used to assess the accuracy of the new algorithm. The various dependencies of the terms in the SST algorithm are investigated. As with the MCSST and CPSST algorithms, the new method has largest errors when applied in situations of abnormal atmospheric structure. The improvement over the MCSST product may initially be only marginal, but with the advent of the more precise data from the Along Track Scanning Radiometer (ATSR) a more accurate global SST product may be possible.     

Measuring sea surface temperature from satellites: a ground truth approach

Determining the correction for atmospheric attenuation is a major problem in processing thermal infrared digital data from very high resolution radiometers aboard NOAA Polar Orbiting Satellites. An empirical equation for estimating this correction is developed. The coefficients of the equation are determined by using regression techniques and comparing satellite observations to sea surface temperature measurements. Although there is not sufficient data to fully evaluate this procedure, initial satellite measurements are within 0.5°C of independent sea surface temperature measurements.     

Use of satellite-derived emissivity to detect coalfire-related surface temperature anomalies in Jharia coalfield,India

Among natural geo-hazards, spontaneous combustion of coal is unique in nature but common in most coal-producing countries. Coalfires can occur in coal seams and stockpiles of coal at ambient temperature in certain conditions, e.g. those concerning coal type, exposed area and moisture content. Once started, coalfires are difficult to extinguish and sometimes cannot be controlled. In addition to burning millions of tonnes of coal, the fires have enormous negative impacts on local and global environments. In the field of coalfire study, remote sensing is used as a powerful tool to detect and monitor coalfires. Nevertheless, most remote-sensing coalfire studies are based on a fixed emissivity (0.95 or 0.96) which is contrary to the real representation of the Earth's surface. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived emissivity was used to detect coalfire-related surface anomalies in an Indian coal mining region. Later, the temperature anomalies detected were validated with ground truth data. Additionally, the ASTER-derived emissivity value was used to extract surface temperatures from Landsat Enhanced Thematic Mapper Plus (ETM+) thermal infrared (TIR) data.     

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.     

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.     

Inter-comparison of various global sea surface temperature products

In this study, eight global sea surface temperature (SST) products for 2009 are compared to clarify their characteristics. The median of eight daily values, the Ensemble Median as Reference Product (EMRP), is used as a reference product for inter-comparison. The results show that the absolute value of mean differences and the value of root mean square (RMS) differences are higher in single-microwave products such as Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), Tropical Rainfall Measuring Mission Microwave Imager (TMI), and WindSat, than in products such as MicroWave Optimally Interpolated SST (MWOI), Merged satellite and in situ data Global Daily SST (MGD), and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) constructed by merging several SST data. It is of note that the characteristics of SST products depend on the type of SST used within the product, rather than the data source used. A comparison of SST products was also conducted using EMRP and data observed by moored buoys. The results show that only AMSR-E has a warm bias (+0.06°C) while other products have a cool bias (maximum value ?0.10°C). The RMS error of TMI is the highest (0.57°C), and that of EMRP the lowest (0.28°C). Furthermore, the temporal variability between the data in each SST product was compared to those observed by the Kuroshio Extension Observatory (KEO) buoy. Results show that the temporal variability of EMRP corresponds well to that of buoy data, and that the RMS error of EMRP is lower than that of the other SST products.     

The effect of monomolecular surface films on the microwave brightness temperature of the sea surface

Abstract

Airborne microwave radiometer measurements at 1·43 and 2·65 GHz over a sea surface covered with a monomolecular oleyl alcohol surface film and over adjacent slick sea surfaces are presented. The measurements show that at 2·65 GHz the brightness temperature T _B is not affected by the slick, while at 1·43 GHz it drops from 93 K to a minimum value of almost O K. This implies that at 1·43 GHz the emissivity of the slick-covered sea surface is extremely small, similar to a metallic layer, and that this resonant-type phenomenon is confined to a narrow frequency band of width δ?/ ?<0·6.

The theoretical implications of these experimental findings are discussed in the framework of the Debye relaxation theory of polar liquids. It is conjectured that a thin layer of water molecules polarized by the surface film gives rise to an anomalous dispersion, which causes the large decrease in brightness temperature at 1·43 GHz.

The modulus of the relative dielectric constant ε? is estimated to be ≥ 5·2 × 10^?4 and the thickness of the emitting layer ≤1·9 × 10^?4 m for 1·43 GHz. Furthermore, the film-induced surface activation energy is calculated to be 9·18 × 10^?21 J. These values seem reasonable in the light of the theories on the physicochemical structure of surface layers.     

Estimates of sea surface nitrate concentrations from sea surface temperature and chlorophyll concentration in upwelling areas: A case study for the Benguela system

The knowledge of nitrate fields at global or regional scales in the ocean is fundamental for the study of oceanic biogeochemical processes, particularly those linked to new primary production. The estimate of nitrate concentrations from space is generally based on empirical inverse relationships between sea surface temperature (SST) and nitrate concentrations. These relationships, however, are often highly variable spatially and temporally, and hardly applicable to large areas (i.e., larger than a few degrees in latitude). In this paper we propose a new approach specifically developed for areas influenced by upwelling processes. It relates the nitrate concentration to the difference between SST and the estimated temperature of the upwelled water (variable with latitude and season), δT, which is an indicator of the time elapsed since upwelling. This approach is tested for the Benguela upwelling system, and algorithms are developed using in situ data provided by the World Ocean Database 2005 of the NOAA-NESDIS-National Oceanographic Data Center. The results reveal a significant improvement compared to the NO₃-SST relationships, and a single algorithm can be applied to the whole upwelling area (15 to 35°S). Further improvement is gained by coupling this approach with a method that derives sea surface nitrate concentrations from SST and surface chlorophyll a concentration using multiple regression analyses, as proposed by Goes et al. [Goes, Saino, Oaku, Jiang, (1999). Method for estimating sea surface nitrate concentrations from remotely sensed SST and chlorophyll a: A case study for the North Pacific Ocean using OCTS/ADEOS data. IEEE Transactions on Geoscience and Remote Sensing, 37, no. 3 II, 1633-1644].     

Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data

An interactive validation monitoring system is being used at the NOAA/NESDIS to validate the sea surface temperature (SST) derived from the NOAA-12 and NOAA-14 polar orbiting satellite AVHRR sensors for the NOAA CoastWatch program. In 1997, we validated the SST in coastal regions of the Gulf of Mexico, Southeast US and Northeast US and the lake surface temperatures in the Great Lakes every other month. The in situ     

Diurnal variability in sea surface temperature in the Arctic

The formation of diurnal warming events in sea surface temperature (SST) observations in the Arctic is investigated using multiple satellite derived SST products and in situ buoy temperature measurements. Significant diurnal warming events (of the order of several K) are shown to occur even in the Arctic during summer months, when the total daily insolation at high latitudes is, in fact, higher than that at low and mid latitudes. The observed Arctic diurnal warming events are shown to usually happen in persistent low wind conditions, and are more frequent in shallow waters than deep waters. During the studied period of June and July 2008, significant diurnal warming events were observed over most of the studied area, although with smaller spatial extent and reoccurring less often when compared to events reported at low and mid latitudes.