The coastal zone has had a key-role since antiquity in every human activity. It continues to be an area of major human interaction, such as commercial trading and cultural exchange, providing food supplies, serving transportation via sea routes, and most recently, providing places for leisure resorts and the tourist economy to grow [1
]. In terms of modern climatology, urban planning, and legislature for the integrated development of the coastal zone and near-shore land, it is vital to qualify and quantify various factors that may impact any action to be made in these areas [2
The intensity of the physical (ground water discharges [3
], tidal mixing [4
], upwelling phenomena [5
]), chemical (water quality [6
], thermal plume contamination [7
], ocean acidification [8
], eutrophication [9
]), and biological interactions (overfishing [10
], habitat loss [11
], and reconfiguration of communities [12
]) between the coastal zone and the terrestrial environment, both from natural and anthropogenic processes, dictates the necessity of monitoring the coastal ocean. Climate change has the potential to exacerbate other anthropogenic effects on coastal waters and the recent change in sea temperature is considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide [13
]. Furthermore, the Global Climate Observing System (GCOS) considers Sea Surface Temperature (SST), including that of the coastal zone, a vital component of the climate system since it largely controls the atmospheric response to the ocean at both weather and climate time scales and it exerts a major influence on the exchanges of energy, momentum, and gases between the ocean and atmosphere [17
In this context, the temperature of coastal ecosystems and marginal seas need to be properly monitored to allow an improved understanding of their dynamics and detection of changes in their properties. This could not only help to complete the SST climate data record but also assess the impact of implementing environmental protection policies at the land-sea interface. Satellite monitoring provides an essential part of this global observing network but also requires a comprehensive global network of trustworthy and accurate in situ measurements for validation and calibration. The present reality is that the actual sampling of a number of important climate-related variables, including temperature, is unevenly distributed in space or time with a large part of the coastal regions being poorly sampled [18
Previous use of satellite SST to monitor the coastal ocean have been carried out in different regions worldwide, e.g., the English Channel [21
], Canada [22
], Caribbean Sea [23
], Argentina [24
], South Africa [18
], China [7
], Western Australia [25
], the Mediterranean Sea [29
], and the Gulf of California [31
]. The aforementioned studies used several satellite SST products including Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua [22
], MODIS Terra [18
] and Advanced Very High Resolution Radiometer (AVHRR) [7
]. Some studies performed in the coastal zones are close to places of anthropogenic infrastructures such as aquaculture [22
] or nuclear power stations [7
]. Others have focused on areas with both ecological importance and interest to tourism such as coral reefs [23
]. Smale and Wernberg [24
], while testing the hypothesis of whether satellite SST data can be used as a proxy for water temperature in benthic ecology, highlighted the lack of experimental field studies to improve our understanding of temperature-related processes in the coastal zone.
Data acquisition and data processing of coastal satellite SST measurements are also prone to various error sources, e.g., cloud contamination, atmospheric correction errors, sampling errors, surface emissivity, sea surface roughness, and suspended particulate matter (SPM) [31
]. This is a result both of the variable natural characteristics of the coastal zone [33
] and of the poor network of near-shore coastal ocean instruments [18
] when compared to the platforms in the open ocean [34
]. Recently, Brewin et al. [21
] used AVHRR data of high temporal resolution in the coastal zone of Plymouth to evaluate operational SST data at the coastline. They matched their in situ data within 1 h of the satellite overpasses and they chose the closest 1 km pixel from their in situ logger’s location. In the Western Mediterranean Sea, Bernardello et al. [30
] made a similar comparison of MODIS Aqua SST data to in situ temperature data recorded at five different locations, although without the same strict temporal and spatial coincidence applied.
In this study, a similar method to Brewin et al. [21
] was followed, where in situ temperature data recorded in the Underwater Biotechnological Park of Crete (UBPC) were compared with satellite SST data from MODIS Aqua and MODIS Terra instruments. The UBPC is a unique resource in the Eastern Mediterranean since it is a large-scale in situ research infrastructure in the coastal zone of Crete that provides autonomous temperature recordings since 2014. The UBPC started as a biotechnology multi-use infrastructure and the instruments that were installed therein were for the needs of the local environmental monitoring. However, because of the shallow nature of the temperature monitoring installations, and since the location of the UBPC is in a key-spot of biodiversity changes due to Lessepsian Migration and may be prone to climatic changes [35
], it was thought this time series could also potentially be useful for satellite coastal SST validation, and thus, it was decided to exploit the vertical temperature loggers four-year time series for a comparison with the MODIS Aqua and Terra SST data.
In this study, the main aim was therefore to evaluate the data collected from the autonomous in situ data loggers of the UBPC for use in the validation of near-shore satellite SST measurements. The hypothesis that the UBPC data could be useful in this way was tested by correlating the in situ data with MODIS SST satellite data and examining the possible errors and biases related to such factors as instrumentation and proximity to the shore.
This study utilized the four-year data series of the in situ temperature loggers deployed in the Underwater Biotechnological Park of Crete and investigated the data’s suitability for use in the validation of MODIS SST products. For the in situ SSTskin, when compared with the AQUASST4 data, there was a negative bias with mean bias values (δ) of −0.08 and squared Pearson’s correlation coefficient equal to 0.9847 (Table 2
). Concerning the MODIS Terra SST products, the TERRASST4 data showed the same pattern in the statistical tests as the aforementioned MODIS Aqua products (SSTskin-TERRASST4: δ= −0.35, r2
= 0.9689) (Table 2
). Moreover, for the location of this study, the nighttime TERRASST4 data were recorded closer to dusk than the nighttime AQUASST4 data, which were recorded later in the night. There is a significant cool skin at night—cooler than at dusk—resulting in the TERRASST4 data having a greater negative bias (δ = −0,35) than the AQUASST4 data (δ = −0.08) when compared to in situ SSTskin data. As a reference, MODIS Terra overpasses in our region occurred around 20:00 UTC and MODIS Aqua overpasses were around 00:00 UTC, and the latest sunset of the year was around 17:40 UTC. Both satellite datasets overestimated the SSTskin measurement when compared with the in situ data of our study area. Furthermore, the comparison between the satellite cell that contains the location of the loggers and the northern adjacent cell—further from the shore—showed an overall positive bias (Table 3
), meaning that higher temperature values were recorded in the cell closer to the shore. These results are in good agreement with previous studies that compared satellite SST data with in situ collected data [18
]. With such good matchups between in situ and satellite measurements, these results and statistics also suggest a potential for the use of UBPC in situ temperature measurements in satellite SST validation.
Recently, Brewin et al. [21
] compared AVHRR data in a similar way with in situ data of the English Channel off the coast of Plymouth and showed a higher difference in satellite SST retrievals in near-shore waters than in offshore waters. In their study, the differences between satellite and in situ SST data were well-correlated with land surface temperature and solar zenith angle. For their closest to the coastline location the correlation between in situ and satellite SST data was 0.83 (δ = −0.30, RMSE = 1.30), while for an offshore location, the correlation between in situ and satellite SST data was 0.97 (δ = −0.01, RMSE = 0.48). Worth mentioning here, as discussed in their study [21
], the closest satellite pixel to the coastline was closer than 2 km offshore, and potentially influenced by land-contamination, since land may be included within the satellite pixel. For our study, the correlation coefficient was over 0.967, and δ was between −0.35 and −0.08, while RMSE was between 0.42 and 0.76 for the in situ with RS SST comparisons. In general, the correlation results of this study’s comparisons agreed well with the offshore results of Brewin et al. [21
]. This may indicate that the land adjacency effect is low enough to allow these good matchups. However, the bias levels indicate that it is still present, although not to such a high degree as the coastline location for Brewin et al. Even though the study by Brewin et al. [21
] was with a different satellite SST product, this comparison indicates that the same principles and difficulties govern all satellite derived coastal SST measurements and their validation, particularly the proximity to land.
Another recent study by Bernardello et al. [30
], in the Western Mediterranean, is also worth discussing in more detail. This study compared MODIS-Aqua data with in situ temperature data loggers, and pointed to a strong correspondence between satellite and in situ data in their area of study. For their study location, all five in situ datasets, when compared with MODIS-Aqua SST data, had r > 0.98 and −0.27 < δ < 0.24. Their study used a method of reconstructing the temperature for shallow near-shore environments not applicable for satellite validation but for overall variability investigation, intra-seasonal and interannual variability, and unseasonably extreme events. Their results cannot be directly compared with ours, but could provide a direction for future work and comparisons concerning two different regions of the Mediterranean Sea, showing a wider application of satellite SST data reconstruction as a proxy for near-shore habitats monitoring.
With respect to the above discussion—and in fact to all relevant studies for validating coastal satellite SST measurements—this is indicative of some of the major difficulties of gaining consistent and valid satellite SST data in the coastal zone. In contrast with the open ocean SST satellite conditions, in the coastal zone, many extra problems need to be addressed. Not only the very nature of some SST products make it impossible to be of use in the coastal zone, e.g., data collected from MW (microwave) technology sensors have to be 75 km away from land because of large footprints that can overlap land, in contrast to IR (infrared) technology sensors that need to be at least 1 km from the closest shore [18
], but also the characteristics of the coastal zone can result in different land adjacency effects on the satellite SST data. The local characteristics of a certain area must be taken into account, particularly for satellite data validation, and need thorough investigation concerning the local geomorphological characteristics, the prevailing oceanographic and atmospheric conditions, and various other factors. For example, Stobart et al. [27
] mentioned that sites with adjacent estuaries are characterized by higher SST in the summer and lower in the winter than in situ temperature data, due to fresh, cold water riverine inputs that float above the denser but warmer seawater. For the location of this study, the conditions were locally specific, with the UBPC lying about 2 km offshore the northern coast of Crete. The northern coast of Crete runs almost horizontally on a west-east axis and is open to the wave action of the Aegean Sea and the prevailing northwest winds that dictate a prevailing west-to-east surface current for the UBPC location. The wider study location is affected by local winterbourne steams that flow only after rainfall; thus, the fresh water input and the transportation of suspended matter are very limited in the satellite cell that the measurements are taken. Furthermore, with the MODIS cell spatial resolution at 1 × 1 km the distance of the study location from the closest shore being approximately 2 km makes it sufficiently distant from the coast not to be directly contaminated by land. However, the possibility of a systematic difference between extrapolated SSTskin from 7 m and the actual SSTskin temperature can be addressed only with the installation of close-to-surface in situ loggers that will record any stratification phenomena especially during precipitation and/or periods when the local streams dissipate fresh water in the coastal zone.
There is also a need to highlight the impact of autonomous in situ sensors, in terms of the applicability of their operating accuracy and uncertainty. Many studies have used autonomous in situ loggers that come with no traceable certificates of calibration, making them prone to add a large amount of uncertainty to satellite measurement validation if they are not at least compared with a sensor that has a traceable calibration certificate and process. Clearly, calibration requirements may raise the financial cost of a study, or exclude datasets with poor accuracy. In this study, for example, the initial deployment of the sensors was to monitor the local environment of the UBPC and only after the comparison between the HOBO3 and the CTD (including a temperature instrument accompanied by a traceable calibration certificate) resulted in very good correlation statistics (r2 = 0.9970, δ = 0.06, RMSE = 0.21); thus, it was decided to use the HOBO data series for comparison with the satellite SST data. There is another important factor that should be borne in mind, which had an unknown effect on the results of this study when comparing in situ data with RS SST data. This is the difference of depth in the water column where the measurements took place, i.e. in situ SSTskin data were extrapolated to the surface from the 7 m depth temperature logger and the satellite measurements are of the skin layer taken from above the sea. This has introduced a systematic measurements of temperature bias that is difficult to correct for in the present in situ time series, and thus, promotes the reconfiguration of the UBPC temperature array to include sensors at or close to the surface in order to eliminate the extrapolation necessity as much as possible, and even try to compare RS SST data with daytime in situ SSTskin data.
Nevertheless, most of the studies comparing satellite SST with in situ temperature data still concluded that satellite SST is recommended for environmental/biological studies and can be used as a proxy for temperature in near-shore coastal areas [24
]. However, as discussed above and in the introduction, when it comes to the validation of satellite SST measurements, many additional factors have to be taken into consideration. Many of the in situ data are collected from research vessels and are heterogeneous [29
] in size, type, and operating footprint, thus creating variable conditions. Additionally, there are many different types of in situ temperature loggers [29
] deployed at different depths and, apart from having to account for this depth bias, the main problem with them is the traceability of their calibration and estimating the overall uncertainty budget of their measurements. For example, the HOBO Pro V2 loggers that are widely used -including in this study- have a poor accuracy (±0.2 °C) and resolution (0.02 °C), and their uncertainty budget when operated long-term in the field and when evaluated, may turn out to be unacceptable for evaluating RS SST data. In various studies, the accuracy of the in situ temperature loggers was compared with the accuracy of other instruments with traceable calibration certificates [21
]. As Castillo and Lima [23
] mentioned, even the position of a logger in the water is crucial; when the narrower side of the logger—or its attached shader—faces the sea surface, it is prone to minimal heat exposure.
It is clear that in situ temperature data loggers should provide as accurate a ground truthing factor as possible for the validation of satellite data. Furthermore, it is becoming a prerequisite for any in situ measurements to be used for satellite sensor validation that they should be SI-traceable (SI: International System of Units) with an uncertainty estimate for each measurement. This increasing trend is demanded by the space agencies and follows the recommendations of the Committee on Earth Observation Satellites (CEOS) [44
], the Quality Assurance Framework for Earth Observation (QA4EO) guidelines [45
], and the European Space Agency (ESA) Fiducial Reference Measurements initiative [46
], in particular the project for Fiducial Reference Measurements for Satellite derived Surface Temperature (FRM4STS) [47
The above suggests further follow-on work concerning our study area and the UBPC, and an attempt will be made to calibrate all the in situ temperature sensors according to FRM principles and to evaluate the additional uncertainty related to using/modeling measurements at a certain depth for the comparison of satellite measurements of the surface. Now that this study has shown the UBPC’s in situ temperature measurement potential for satellite SST validation, this further effort may help to make them more relevant to the space agencies for operational satellite validation. Furthermore, additional in situ temperature loggers are going to be installed, with some as close to the sea surface as possible to help account for the depth of measurement related biases in the in situ time series, something that could also be assisted by comparing our in situ data with an Infrared radiometer that measures the SSTskin-like Infrared Sea-surface temperature Autonomous Radiometer (ISAR) or Marine-Atmospheric Emitted Radiance Interferometer (M-AERI). For these near-surface sensors, following the recommendations of Castillo and Lima [23
], the installation will be with their long surface vertical to the sea surface in order to be less affected/biased due to direct solar radiation heating. This will also provide the most accurate method for investigating any stratification phenomena in the study area, since it will as much as possible prevent direct solar radiation heating—which, when using unshaded loggers, can result in areas with transparent clear waters, with erroneously high underwater temperature measurements, according to Bahr et al. [48
] and Brewin et al. [49
]. A land-based meteorological station is also going to be installed in the vicinity of the study area, so that it will be possible to investigate and quantify any correlations between atmospheric conditions and satellite SST measurement anomalies, following the methods of Brewin et al. [21
]. Finally, as there are also biases between different satellite missions, each with their own uncertainties [18
], following the UBPC temperature sensor upgrade, a separate inter-comparison study is planned between various satellite SST products and the UBPC data to give an indication of the different correspondence between satellite sensors and products when compared to the same in situ data.