Comparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea

Alvera‐Azcárate, Aida; Troupin, Charles; Barth, Alexander; Beckers, Jean-Marie

doi:10.1007/s10236-011-0403-x

Cited by 10 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous satellite SST to in situ ST comparison conducted over 1 yr in the Mediterranean Sea indicated a large bias between in situ and satellite data (Alvera-Azcárate et al 2011). The differences between the wide variety of in situ sources (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…The present study goes beyond the work of Sacris tán- Soriano et al (2012) and Alvera-Azcárate et al (2011) because it considers a period that spans more than 6 yr and a variety of coastal environments with different topographies and hydrodynamic regimes. Additionally, we focused our analysis on 4 different modes of variability in order to cover a wide spectrum of frequencies for events with ecological importance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of remote-sensing SST and in situ seawater temperature in near-shore habitats in the western Mediterranean Sea

Bernardello¹,

Serrano²,

Coma³

et al. 2016

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

Remote sensing of sea surface temperature (SST) is widely used in climate science because it provides a quasi-synoptic coverage of the ocean. However, the use of these data for near-shore habitats is hindered by the proximity of the coast, therefore further investigation is needed. We compared remote-sensing SST from the MODIS sensor (aboard the Aqua satellite) to near-shore seawater temperature (ST) recorded in situ with data loggers at 5 locations in the western Mediterranean Sea. In situ ST data were collected at 5 m depth over a ~6 yr period and at depths below 5 m at 3 of the locations. We evaluated the suitability of MODIS to represent the temperature at shallow subtidal depths relative to different modes of variability. MODIS reproduced seasonal variability with high correlations (r > 0.98) and biases (0.59 ± 0.03°C) only slightly higher than the accuracy of the loggers (0.50°C). MODIS also captured interannual variability with no systematic biases. When evaluated for intra-seasonal temperature variability, MODIS showed limited biases (up to 0.79°C) with a tendency to overestimate the variability (between 4 and 64%) in both cold and warm seasons. Finally, MODIS over-/underestimated only the most extreme unseasonably cold/warm events (by 1.51 and −0.79°C, respectively). The observed limited differences between the 2 methods can be explained by the particular hydro dynamics of the area and by methodological constraints. Overall, MODIS SST data proved to be a reliable proxy for near-shore ST in the western Mediterranean Sea, and are thus considered suitable for studies requiring temperature reconstruction in shallow near-shore environments.KEY WORDS: MODIS-Aqua · Seawater temperature · Shallow-water ecosystems · Near-shore ecology · Mediterranean Sea · Balearic SeaResale or republication not permitted without written consent of the publisher

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison of remote-sensing SST and in situ seawater temperature in near-shore habitats in the western Mediterranean Sea

Bernardello¹,

Serrano²,

Coma³

et al. 2016

Mar. Ecol. Prog. Ser.

View full text Add to dashboard Cite

show abstract

“…3a, Table 2). This value was substantially larger than the range of the cool bias errors, about -0.1 to -0.2°C, found in deeper water columns (Donlon et al 2002;Barton 2007;Alvera-Azcárate et al 2011) and it should be taken into account in studying the behaviors of corals as the corals respond to temperature changes of even a few degrees (Lough 2012). The RMSE between SST sat and SST bulk in these shallow waters in the DSA, ±0.73°C, was larger than that, ±0.59°C, reported in the open northern South China Sea by about 25% (Pan et al 2015).…”

Section: Discrepancy Between Sst Sat and Sst Bulk At The Dsamentioning

confidence: 60%

“…28, No. 3, 517-524, June 2017 magnitude of these mean bias errors can vary between about -0.2 to +0.2°C (Emery et al 2001;Li et al 2001;Donlon et al 2002;Barton 2007;Alvera-Azcárate et al 2011). It is widely recognized that operational calibration algorithms are needed to bring SST sat and SST bulk back to agreement (Schluessel et al 1990;Walton et al 1998;Kilpatrick et al 2001;Donlon et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Validation of the remotely sensed nighttime sea surface temperature in the shallow waters at the Dongsha Atoll

Pan¹,

Wong²,

DeCarlo³

et al. 2017

Terr. Atmos. Ocean. Sci.

View full text Add to dashboard Cite

Fine scale temperature structures, which are commonly found in the top few meters of shallow water columns, may result in deviations of the remotely sensed night-time sea surface temperatures (SST) by the MODIS-Aqua sensor (SST sat ) from the bulk sea surface temperatures (SST bulk ) that they purport to represent. The discrepancies between SST sat and SST bulk recorded by temperature loggers at eight stations with bottom depths of 2 -20 m around the Dongsha Atoll (DSA) between June 2013 and May 2015 were examined. The SST sat had an average cool bias error of -0.43 ± 0.59°C. The bias error was larger in the warmer (> 26°C) waters which were presumably more strongly stratified. The root mean square error (RMSE) between SST sat and SST bulk , ±0.73°C, was 25% larger than that reported in the open northern South China Sea. An operational calibration algorithm was developed to increase the accuracy in the estimation of SST bulk from SST sat . In addition to removing the cool bias error, this algorithm also reduced the RMSE to virtually the same level as that found in the open northern South China Sea. With the application of the algorithm, in June 2015, the average SST in the lagoon of the DSA was raised by about 0.5°C to 31.1 ± 0.4°C, and the area of lagoon with SST bulk above 31°C, the median value of the physiological temperature threshold of reef organisms, was increased by 69% to about three quarters of the lagoon.

show abstract

“…For various application such as weather prediction, ocean estimating, climate research studies, marine fishery resources high quality of SST datasets are necessary. Sea surface temperature can be obtain with different sensors but these sensors do not provide a similar estimation of the SST because of each sensor type [1]. Sea surface temperature maps are important for commercial and fishing communities as well as provide critical information for gases between ocean and atmosphere [8].…”

Section: Introductionmentioning

confidence: 99%

Satellite Derived Sea surface temperature fronts in relation with Tuna catch In EEZ of Pakistan

Abdullah¹,

Malik²,

Siddiqui³

et al. 2018

Pakistan Journal of Engineering, Technology & Science

View full text Add to dashboard Cite

Sea surface temperature (SST) is an important parameter in marine ecosystem studies as its relations of Fishery and other marine resources. In this study SST fronts have also been studied with relate to tuna fish catch data of April and August 2014 was acquired. Satellite derived MODIS daily products have been used to derive thermal fronts in the exclusive economic zone (EEZ) of Pakistan. Research results indicated that the Sea surface temperature gradually changed from 22C to 24C where Tuna catch is high and By Catch is low in frontal region. The further Relationship between these two data are discussed in this study and also made recommendations for in what way these two datasets should be handled. Remote sensing data and GIS tools are efficient and less time consuming for mapping and classifying sea surface temperature in a broader way. Survey of fishing resources is really time consumed and costly, Satellite Remote sensing data shows a promising tool to monitor fishery resources in a cost effective manner. Satellite data play an important role to identify fish aggregation zones and these techniques could also be used to forecast potential fishing zones by measuring oceanic parameters which influence on fish distribution on a broader scale and these techniques can help to local fisherman and fishery organizations to observe fishery resources.

show abstract

Comparison between satellite and in situ sea surface temperature data in the Western Mediterranean Sea

Abstract: A comparison between in situ and satellite sea surface temperature (

Cited by 10 publications

References 20 publications

A comparison of remote-sensing SST and in situ seawater temperature in near-shore habitats in the western Mediterranean Sea

A comparison of remote-sensing SST and in situ seawater temperature in near-shore habitats in the western Mediterranean Sea

Validation of the remotely sensed nighttime sea surface temperature in the shallow waters at the Dongsha Atoll

Satellite Derived Sea surface temperature fronts in relation with Tuna catch In EEZ of Pakistan

Contact Info

Product

Resources

About