Seasonal and Spatial Variability of SST Using MODIS Data: the Case Study of Aegean ...

International Journal of Remote Sensing Applications (IJRSA) Volume 5, 2015 doi: 10.14355/ijrsa.2015.05.003

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Seasonal and Spatial Variability of SST Using MODIS Data: the Case Study of Aegean Sea Kontsantinos Topouzelis1, Stefani Varnava2, Andreas Georgiou2* University of the Aegean, Dept. of Marine Science, University Hill, Mytiline 81100, Greece

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University of the Aegean, Dept. of Geography, University Hill, Mytiline 81100, Greece

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topouzelis@marine.aegean.gr; 2stefvarnava@gmail.com; 3angeocy@gmail.com

1

Abstract This paper focuses on the analysis of spatial and temporal variability of Sea Surface Temperature (SST) derived from Aqua Moderate Imaging Spectrometer (MODIS) infrared imagery in the Aegean Sea. Data were organized to examine both seasonal and monthly variation of SST during 2005 - 2008. The four year period was chosen as a first approach to address and describe this phenomenon. A descriptive statistical analysis was performed, while hot spots and SST anomalies for each month and season were identified. The results show a smooth monthly variation of SST with February to be the coldest month while July the hottest as expected. The year with the higher average SST value was 2007 with 25.1oC while 2006 presents the minimum average value. In addition, the high resolution maps of 1 km that extracted from the analysis with a wide spatial coverage, allows to the detail representation of SST in the region. This helps to produce a rich eddy field and it allows monitoring the outflow of Black Sea Waters through the Dardanelles Strait.

Keywords Aegean Sea; MODIS; Sea Surface Temperature; Remote Sensing

Introduction The temperature of the sea surface has been identified as an important parameter of the natural environment, governing processes that occur in the upper ocean. Biological activity, exchange of heat, gas and momentum with the atmosphere depend on this oceanic layer and its properties [Ioanna et al, 2012]. Therefore, Sea Surface Temperature (SST) is an important indicator of the state of the earth’s climate and is a key parameter for understanding the hydrological cycle due to the interaction between air and sea temperature [Ioanna et al., 2012; Liou G.R and Kuo T.H., 1994].

The main problem encountered in a variety of water temperature studies is the lack of monitoring stations and buoys that result in both spatial and temporal gaps in SST records. The above stated monitoring difficulties can be affectively solved by the use of remote sensing which allows one to obtain spatially detailed, regularly and continuously repeated datasets. Satellite infrared (IR) radiometers provide a wide spatial coverage record of SST in near-real time, uniquely resolving existing difficulties in monitoring with traditional ship observation methods [Igor K. et al., 2014; Casey S.K. and Cornillon P. 1999]. The continuous acquisition of satellite data improves the quality of the information extracted from a remotely sensed data set.

The variability of the temperature in the upper layer of the Aegean Sea is one of the major hydrological processes that affect both ecosystem and productivity in Easter Mediterranean [Rayner A.N. et al. 2003]. There are some published papers analyzing satellite SST data sets for the Aegean Sea within particular topics. More specific, an overview of the oceanography of the Aegean Sea is given in Poulos et al. [Poulos et al., 1997] where they employed climatological values for the hydrological characteristics and the air-sea and land-sea interaction parameters. Rivo U. And Liis S. (2007) describe the surface warming of Mediterranean Sea and climate changes with related issues including Aegean Sea using AVHRR thermal data. Nikolaos S. et al. (2011) and Seniz U. (2005) used also AVHRR time series in combination of in situ data to examine the long term variability and spatial distribution of SST. Finally, Zevrakis et al. (2000) examined the influence of Black Sea Water (BSW) and river discharges in sea in North Aegean using in situ measurements over the region area. This influence was also evident by the studies of Seniz U.

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(2005) and Kourafalou V. and Barbopoulos K. (2003) that used a climatological run of a regional ALERMO model that interpolated onto the North Aegean Modela grid. However, up to now there is still considerable lack of scientific works related with the spatial distribution and detailed estimation in high resolution of SST and the identification of SST anomalies (ASST) over the entire Aegean region derived by time series satellite data sets.

The aim of this study is to utilize SST Level-2 records from the Moderate Image Spectrometer (MODIS) onboard Aqua satellite to investigate the water temperature regime of the Aegean Sea and to assess spatial, seasonal and annual water temperature variations. The study will thus contribute to monitor the interaction between the study area with BSW and Levantine basin and to identify ASST. The derived 1km spatial resolution images improve the ability to detect any regional changes and allow the detail spatial representation of SST. The framework comprises a combination of already established methods and tools for SST estimation, spatial descriptive analysis and visualization techniques. The SeaDASTM software has been chosen as the analysis tool and Geographical Information Systems (GIS) as an integrated platform for modeling and presentation. This work consist a further step of older works for estimation SST, as it takes into account a bigger time series satellite data with different origin than others and entire Aegean Sea as a study area. Innovative aspects comprise a straightforward integration, with high resolution resulted final maps in addition with the developed methodology approach which is flexible for applying “what if” scenarios.

Material and Methods Study Area The Aegean Sea is a distinct sub-system of Eastern Mediterranean Sea; it is bounded by the Greek mainland at north and west, by the Turkish coastline at east and by the islands of the Cretan arc at the south. Is connected with the Sea of Marmara through the Dardanelles Strait and, hence, to the Black Sea through the Bosporus Strait [Fig.1].

The Aegean Sea, displays prominent hydrodynamic features involving all time and space scales such as strong baroclinic and topography – induced currents, mesoscale eddies, upwelling regions and frontal zones [Skliris et al., 2010]. In addition, the Aegean Sea it is characterized by irregular coastline (gulfs, islands, straits) and complex bottom topography with complex hydrology and circulation.

FIG. 1 AREA OF INTEREST; A = BLACK SEA; B = BOSPORUS STRAIT; C = SEA OF MARMARA; D = DARDANELLES STRAIT; E = CRETAN ARC; F = LEVANTINE BASIN

Satellite Imagery The spatiotemporal variability of the Aegean Sea is analyzed by means of a four year (2005 - 2008) series of

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satellite-derived SST images. Daily MODIS infrared radiometer data carrying by sun synchronous Aqua satellite were used with an ascending pass in afternoon (1:30 PM). MODIS Level 2 sea surface temperature product provides a long wavelength (11-12μm, SST) and a short wavelength (3-4μm, SST4) data at 1x1km resolution. The SST4 algorithm for daytime products is not used as the measured radiance at short wave infrared wavelength is contaminated by reflected sun radiation.

The SST data obtained from the NASA OceanColour web servers in Hierarchical Data Format (HDF). Standard MODIS cloud mask encrypted in the Level-2 flags is further used to eliminate cloud-contaminated pixels. In addition, for quality assessment, good quality flag (QL-0) was chosen to filter MODIS SST product, which includes sun glint mask and land contaminations. The application of this flag increases the reliability of SST estimate [Anu R. and Markus R., 2008].

Data were organized in a way to examine both seasonal and monthly variation of SST. The examinations of seasonal variation for 2005 to 2008, obtained by 810 satellite images (about 16 images per month) while the monthly variation of 2008 obtained by 226 satellite images (about 18 images per month). The selection of the images based mainly to the optical quality of the available images over study area and in a way to archive more repsresentive values for each case. The mean value was calculated according the sample mean algorithm (Eq.1). Ῡ=

1

n

∗ ∑ni=1 Yi

(1)

For the most accurate SST retrieval over the Aegean Sea a long-term series of data is needed. However, in this study we examine the standard MODIS L2 SST products in a way to achieve a detail high resolution Level 3 data over the study area that will improve the ability to detect any regional changes and will be helpful to monitor the ASST. Methodological Framework The estimation of sea temperature from satellite data includes radiometric correction, georeferencing, SST calculation, land mask and cloud contamination. These steps may overlap or interact among them without estimating false values of SST [Chavula G. et al, 2009].

The present methodological framework considers the following steps for the estimation of SST (Fig.2): (i) Organization and Development of geo-database; (ii) Study area extraction; (iii) Quality flag selection; (iv) Exclusion of false value cells (cloud, glint or land) from further analysis to eliminate calculation errors; (v) re-project in Mercator projection for better visualization [Robinson S.I., 2004]; (vi) Calculation of SST statistics and ASST; (vii) Visualization and presentation of results.

FIG. 2 FLOW CHART OF PROPOSED METHODOLOGY

Descriptive statistical analysis is then performed in means of standard deviation, minimum, maximum and mean.

Descriptive statistics provide a useful summary of the data as they provide a historical account of return behavior.

The ASST is then computed from the comparison of means from years versus 2008.

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To analyze MODIS data, the SeaWiFS Data Analysis System (SeaDAS) was used in a combination with ArcGISTM. SeaDAS is a comprehensive satellite data processing software package for the processing, displaying, analysis and quality control of ocean parameters such as SST and ocean color products. Is currently available for Linux platform and also runs with VM-ware appliance on Windows platform. The GIS platform is then used to perform extra analysis and to apply visuzlization techniques.

Results Monthly Variation Monthly variation of SST derived from mean values of each month respectively. Fig.3 presents the distribution of value pixels versus SST per month, where the seasonal cycle is clearly evident. The majority of temperature values in November through April are below 18oC increasing to ~24oC in May and reaching maximum over 30oC in July through August until decreasing again in September to October.

This noticeable shift of histogram from January to December is more evident in the resulted thematic maps that present the spatial distribution of SST (Fig.4). There is a clear temperature contrast between the gulf shallow waters and the open sea waters over study area. The shallower areas near coastlines, especially from June to September, are warmer than the open sea. This phenomenon reverses mostly from January to April in North part, due to the inflow of fresh cold water from the Black Sea as it shows. The temperature equality between the shallow water and the sea is observed in May and October. Also, the spatial distribution of the mean SST shows a clear southward gradient with minimum values around the Dardanelles Strait and in the northeastern part of the Aegean Sea, and maximum values in the Cretan Sea. Spatial variability is mainly determined by the input of external surface water masses, such as Black Sea cold waters entering the basin through the Dardanelles Strait and the Levantine warm waters entering the Cretan Sea through the eastern Cretan arc straits. Finally, as it shows February presented as the coldest month where most of the values lie at ~12oC while the warmest is August with most of the values at ~28oC.

FIG. 3 DISTRIBUTION OF PIXELS VS SST

FIG. 4 MONTHLY SPATIAL VARIATION OF SST OVER 2008

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Seasonal Variation and ASST In this section, a four year simulation (2005-2008) for seasonal variation of sea temperature was performed. The definition of the seasons is as follows: winter (December – January – February); spring (March – April –May); summer (June – July – August); autumn (September – October – November). Fig. 5 shows the mean seasonal SST variation of the four years where mean value, minimum and maximum where calculated. In addition, the low standard deviation values indicate that the data points tend to be very close to the mean. The SST shows a sinusoidal seasonal cycle, with persistent seasonal warming trend from winter (December) to summer (August). As expected, warm values are presented in summer with an average of 28oC while cold in winter with an average of 14oC. More specific the cycle can be structured into three parts with the following characteristics: (a) From January through March the mean temperatures behave irregularly with no clear trends; (b) Starting in April, the temperatures rise rapidly and are peaking between July and August; (c) From September through December the surface temperature values decrease regularly.

To visualize the SST variation and the spatial distribution of values, thematic maps for each season with mean values created for each year. An example is presented in Fig.6 where winter is visualized for each year. Significant differences are observed mostly in the north part of the study area. As was previous illustrated in monthly variation, the influence of BSW is crucial on the contribution of SST which is mostly evident in 2006. The Chalcidice gulf and mostly the Limnos Basin which is located closest to the Dardanelles exit than the other basins, reveal strong BSW influence with low values. The low values during 2006 are probably related to other factors such as the intrusion of Levantine waters, affecting the vertical mixing and eventually the formation of the upper mixed layer [Yannis S.A. et al., 2012]. In addition, the winter of 2008 seems to be the hottest of them all, with high values to be located mostly in the greater Crete area at the south.

Spatial temperature differences also observed during the rest three seasons. In all of them the fresh waters from BSW influence SST in the Eastern part of the Aegean Sea. On the contrary waters close to Greek mainland remain relative stable because they only influenced by river basins, not able to alter significantly the SST. Finally, SST spatial domain present a stable increase towards the southern direction (difference of 8-12 oC with the northern part).

The seasonal variation of the four year period is clearly presented in Fig.7, where SST variation is smooth but with significant differences among the years. The temperature differences rise from 0.5 to 1.0oC among the years, while the curve of the graph is presented almost the same from year to year. Exception is 2006 that presents the coldest year of them all while the 2008 the hottest one especially in summer.

FIG. 5 SPATIAL DISTRIBUTION OF SST DURING WINTER FROM 2005 TO 2008; A = LIMNOS BASIN; B = CHALCIDICE GULF; 1 = EVROS RIVER; 2 = NESTOS RIVER; 3 = AXIOS RIVER; 4 = ALIAKMONAS RIVER;

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FIG. 6 AVERAGE 4 SEASONAL CYCLE WITH MINIMUM AND MAXIMUM SST'S OF THE STUDY AREA

FIG. 7 AVERAGE SEASONAL CYCLES FOR EACH YEAR FROM 2005 TO 2008

FIG. 8 ANOMALY MAPS FROM 2005 TO 2008

However the examination of SST differences among the years cannot obtain through the resulted maps as the small variation of temperature among the years is not clearly visualized. To address that, anomaly maps created to describe better the phenomenon and to extract better results. Thus, each season of each year compared with 2008 respectively and the results are presented in Fig.8. The blue color indicates negative SST changes; the white color indicates no changes and orange indicates positive changes. As it shown, the winter of 2006 is the coldest one while the winter of 2007 the warmest one with most of the high values to be presented in the east coasts and in open sea.

At spring, high variations are observed at south and north of the study area in Chalcidice gulf and in Crete Island respectively with 2005 to be the warmer by 2oC. The year 2006 present positive values over the islands of central Aegean while there are no considerable differences over 2007 and 2006. In summer period, there is a significant difference of 3oC among the cold year 2005 and hot 2008, while the distribution of positive values in south Crete and in central Aegean indicating the rise of temperature. Finally, for autumn no considerable differences presented among years except that 2006 seems to be cooler than 2008 following the previous pattern.

Discussion and Conclusions The model implementation described in this study addresses the spatial and temporal variability of the SST over the Aegean Sea using a four year MODIS data onboard Aqua satellite. The importance of this kind of study in the region is the detailed representation of SST in high resolution that allows monitoring the temperature in time and space. Although there are limitations of the dataset due to the short time-series considered here, however it can be very helpful for the visualization and identification of any connections of study area with BSW or other inflows. It also helps to identify any trend of SST over the analysis period and to visualize the spatial distribution of SST over the study area.

It was found that the seasonal cycle can be structured into three parts: an irregular development of SST during the

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winter, a steady temperature increase during the spring and summer months that shows maximum values between July and August, and a linear decrease in the values starting in September and continuing for the rest of the year. It was also found that although a similar cycle exists for all the years of the observation period, there exist significant differences regarding the timing of the cycle. In addition SST anomalies identified and mapped with 2006 to be the coldest year and the 2008 the warmest.

In terms of spatial distribution it was found that there is a clear temperature contrast between the gulf shallow waters and the open sea waters over study area. The shallower areas near coastlines are warmer than the open sea while at North part this phenomenon reverses mostly in winter due to the inflow of cold fresh water from the Black Sea. Also, the spatial distribution of mean SST shows that there is a clear southward gradient with minimum values to be presented around the Dardanelles Strait and in the northeastern part of the Aegean Sea and maximum values over the Cretan Sea. Spatial variability is mainly determined by the input of external surface water masses, such as Black Sea cold waters entering the basin through Dardanelles Strait in North and Levantine warm waters enterning the Cretan Sea throught the eastern Cretan arec straits at South.

Current findings regarding the seasonality, the spatial extend of SST and the influence of nearby water inflows are in very good agreement with results from previous studies [Poulos et al., 1997; Yiannis S.A. et al., 2012a; Yiannis S.A. et al., 2012b; Androulidakis Y. and Kourafalou V., 2011; Skliris N. et al., 2010]. Several large rivers (such as Evros, Nestos, Axios and Aliakmonas – Fig. 6) discharge large volumes of turbid fresh water in the Aegean Sea that can affect SST values. Further, the BWS spreading affects temporal and horizontal spatial variability of the SST. The BSW signal is strongly apparent over the entire northeastern area, during the winter and in the beginning of autumn. Northern areas, near Chalcidice gulf and mostly near Limnos Basin which are located close to the Dardanelles exit are reveal strong BSW influenced with low values. In the other hand, southern areas are strongly affected by Levantine warm waters entering the Cretan Sea through the eastern Cretan arc straits, as well by the spatial variability of air-sea heat fluxes and the upward vertical transports of intermediate (cold) water due to turbulent mixing/ or upwelling process [Nikolaos S. et al., 2011; Skliris N. et al., 2010].

The implementation of this kind of models and descriptive regional studies is a very challenging task. In order to improve the implementation, a more thorough validation of model outputs should be performed. This would require a further comparison of model outputs with available high quality is situ measurements, buoy or other satellite data. Comparison for longer periods is also needed to test model response to changes. Another important improvement will be the use of more accurate analysis approach and one of the techniques that refer to Skliris N. et al. (2010) and Nikolaos S. et al. (2011) is the use of the Empirical Orthogonal Function (EOF). Skarlatos et al. (2013) in the other hand suggest Fourier analysis in order to investigate unknown patterns on a time series data. Another aspect that should be taken into account for future analysis is the use of other satellite databases such as SeaWiFS ocean colour data [Skliris N. et al., 2010] or AVHRR data [Poulos et al., 1997] and a combination of them that should allow a more accurate picture of the study region. In addition, data from both MODIS onboard Terra and MODIS onboard Aqua could use to cover all time passes of satellites (daytime & nighttime) over the area of interest. Also, meteorological conditions have to take into account as significantly affect the surface physical parameters and thus the SST [Yannis S.A. et al., 2012a]. The ASST is another parameter that needs further analysis as indicates how much temperatures depart from what is normal for that time of year. A long time series data for that reason would be helpful in way to identify the anomalies and correlate them with any climate changes over the years. Finally, the division of the study area in sub areas would also be helpful for further and more detailed analysis of their characteristics. That would help to understand better how the system works in these areas, what are the interactions among them and also identify and analyse any outliers.

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