An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

Abstract

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

An Assessment of Computational Fluid Dynamics as a Tool to Aid the Design of the HCMR-Artificial-ReefsTM Diving Oasis in the Underwater Biotechnological Park of Crete

“…The environmental monitoring at the UBPC utilizes in situ scientific loggers (e.g., CTDs, ADPs, temperature logger arrays) to give data on such parameters as temperature, salinity, and current speed and direction. For our study location, available remote sensing datasets were compared with in situ autonomous temperature logger datasets resulting in highly correlated match-ups [79], thus providing confidence in using remote sensing data as a proxy for ecological monitoring and also as a source for former years' local environmental and climatic information. Further work on the integration of these environmental variables should assist future numerical simulations of the conditions around the AR and the diving oasis to be as accurate and analytical as possible, comprising and modeling all the available input parameters.…”

Deep Learning-Based Time Series Forecasting Models Evaluation for the Forecast of Chlorophyll a and Dissolved Oxygen in the Mar Menor

“…The Mar Menor is a hypersaline coastal lagoon with a surface area of 135 km 2 , the largest in the Iberian Peninsula and one of the largest coastal lagoons in Europe. Its total water volume is 653 hm 3 , and its maximum depth is 7 m. As shown in Figure 1, it is located on the southeastern coast of Spain, specifically in the so-called Campo de Cartagena, which occupies an area of 1609 km 2 , draining into the Mar Menor.…”

“…Geospatial technologies are used to carry out this monitoring, as they provide accurate, real-time data on the conditions of the sea and its watershed. These technologies include various Copernicus satellites [2], drones, oceanographic buoys, and multi-parametric oceanographic probes equipped with sensors (CTDs) [3] that measure multiple parameters such as temperature, salinity, turbidity, chlorophyll a concentration, dissolved oxygen and organic matter content, sea level oscillations, and surface runoff inputs from the catchment. These data are collected and analyzed using data science to obtain information on trends and patterns in the Mar Menor ecosystem [4].…”

Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis

“…Furthermore, the determination of accuracy and precision often does not follow a common standard. More importantly, the values provided are not the same as in situ, as values significantly depend on factors such as sensor age, exposure time, biofouling, and other external factors (Callow and Callow, 2011;Androulakis et al, 2020).…”