Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea)
| Autor: | El Khalil Cherif, Patricija Mozetič, Janja Francé, Vesna Flander-Putrle, Jana Faganeli-Pucer, Martin Vodopivec |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Chlorophyll a 010504 meteorology & atmospheric sciences Geography Planning and Development coastal waters Gulf of Trieste Aquatic Science 01 natural sciences Biochemistry chemistry.chemical_compound chlorophyll-a udc:574 hydrobiology Calibration hidrobiologija TD201-500 0105 earth and related environmental sciences Water Science and Technology Remote sensing Water supply for domestic and industrial purposes Instrument Data 010604 marine biology & hydrobiology Spectral bands Hydraulic engineering strojno učenje Random forest machine learning chemistry Ocean color klorofil-a obalne vode Environmental science Satellite Seawater Sentinel-3 OLCI TC1-978 Tržaški zaliv |
| Zdroj: | Water Volume 13 Issue 14 Water, Vol 13, Iss 1903, p 1903 (2021) Water, vol. 13, no. 14, 1903, 2021. |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w13141903 |
| Popis: | While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations. |
| Databáze: | OpenAIRE |
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