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Artículo

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

Arena, MaximilianoIcon ; Pratolongo, Paula DanielaIcon ; Loisel, Hubert; Tran, Manh Duy; Schaffer Ferreira, Jorge, Daniel; Delgado, Ana LauraIcon
Fecha de publicación: 12/02/2024
Editorial: Frontiers Media
Revista: Frontiers in Remote Sensing
ISSN: 2673-6187
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Oceanografía, Hidrología, Recursos Hídricos

Resumen

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouthof the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastalsystem defined by its high suspended loads and relatively low colored dissolvedorganic matter. The typically high turbidity of these waters makes the BBAERONET-OC distinctive within the AERONET-OC network stations, providingexceptional opportunities not only for the validation of atmospheric correctionalgorithms but also for the development of regional algorithms for coastalcomplex waters. A SeaWiFS Photometer Revision for Incident SurfaceMeasurements (SeaPRISM) instrument was deployed in January 2020 in theupper rail of a Mareograph Tower, a 15m tall structure, located 10 miles awayfrom the coast in optically deep waters. In this work we used the remote sensingreflectance (Rrs) derived from the BB-AERONET-OC measurements along with insitu hyperspectral radiometric data to classify optical water types (OWTs). Weassigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., andOWTs were further characterized with the concentrations of optically significantcomponents (chlorophyll-a and suspended particulate matter) and inherentoptical properties (absorptions of phytoplankton, non-algal particles, anddissolved organic matter), retrieved from water samples obtainedsimultaneously with radiometric spectra. Based on a match-up exercise within situ data, different schemes of atmospheric correction methods were appliedto Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operationalproduct OLCI Level 2 European Space Agency (ESA) standard (hereafter referredto as “Standard Neural Network (NN)”) proves to be the most suitable atmosphericcorrection algorithm, which was then used to describe spatial patterns andtemporal variability of the different OWTs in the region. The BB-AERONETOCsite is located in a sharp transition between estuarine and coastal watersthat present contrasting optical conditions: OWT 4 dominates over time (73.72%of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5,which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts theeutrophic coastal waters of the inner shelf. The variability of OWTs and the relativecontribution of organic and inorganic compounds to the suspended materialwould be mostly related with the prevalence of northwest winds in the area,which would drive the export of estuarine sediments to the shelf.
Palabras clave: AERONET-OC , optical water types , atmospheric correction , Sentinel 3 OLCI , coastal waters
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution 2.5 Unported (CC BY 2.5)
Identificadores
URI: http://hdl.handle.net/11336/233425
URL: https://www.frontiersin.org/articles/10.3389/frsen.2024.1305787/full
DOI: http://dx.doi.org/10.3389/frsen.2024.1305787
Colecciones
Articulos(CERZOS)
Articulos de CENTRO REC.NAT.RENOVABLES DE ZONA SEMIARIDA(I)
Articulos(IADO)
Articulos de INST.ARG.DE OCEANOGRAFIA (I)
Citación
Arena, Maximiliano; Pratolongo, Paula Daniela; Loisel, Hubert; Tran, Manh Duy; Schaffer Ferreira, Jorge, Daniel; et al.; Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca; Frontiers Media; Frontiers in Remote Sensing; 5; 1305787; 12-2-2024; 1-17
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