Technical note: Algal Pigment Index 2 in the Atlantic off the southwest Iberian Peninsula: standard and regional algorithms

Abstract

Abstract. In this study, Algal Pigment Index 2 (API2) is investigated in Sagres, an area located in the Atlantic off the southwestern Iberian Peninsula. Standard results provided by the MEdium Resolution Image Spectrometer (MERIS) ocean colour sensor were compared with alternative data products, determined through a regional inversion scheme, using both MERIS and in situ remote sensing reflectances (R rs ) as input data. The reference quantity for performance assessment is in situ total chlorophyll a (TChl a) concentration estimated through a phytoplankton absorption coefficient (i.e. equivalent to API2). Additional comparison of data products has also been addressed for TChl a concentration determined by high-performance liquid chromatography. The MERIS matchup analysis revealed a systematic underestimation of TChl a, which was confirmed with an independent comparison of product map analysis. The study demonstrates the importance of regional algorithms for the study area that could complement upcoming standard results of the current Sentinel-3/OLCI space mission. IntroductionThe MEdium Resolution Image Spectrometer (MERIS) space sensor, operated by the European Space Agency (ESA) on-board the ENVISAT platform from 2002 to 2012, has been continuously supported by investigations for the assessment and improvement of data products. Commissioned studies include the validation of radiometric data such as the R rs Kajiyama et al., 2014), as well as the analyses of derived product maps D'Alimonte et al., 2014;Cristina et al., 2016b). These MERIS validation activities have established an important basis to address Earth observation (EO) capabilities through the Ocean Land Colour Instrument (OLCI) sensor launched on the Sentinel-3 satellite in February 2016. OLCI data products are the main component of the Copernicus European programme to monitor the marine environment, and the retrieval of chlorophyll a (Chl a) is a core task of the Sentinel-3 space mission. Chl a is needed to estimate the phytoplankton biomass in the ocean and to contribute to a variety of interrelated investigations and applications, including climate data records, environmental legislation, and a number of economic activities such as fisheries and aquaculture. After the removal of the atmospheric contribution to the signal recorded at the top of the atmosphere, Chl a can be estimated from the bottom-of-atmosphere (BOA) R rs values, using the standard approach with polynomial algorithms based on band ratios of the input radiometric quantities. The corresponding MERIS data product is denoted Algal Pigment Index 1 (API1) (Morel and Antoine, 2011). The use of band ratio is based on the assumption that seawater optical properties are driven by Chl a. A tendency towards overestimation has, however, been documented in optically complex marine conditions . This can occur when optically active constituents, such as coloured dissolved orPublished by Copernicus Publications on behalf of the European Geosciences Union.

Patterns and drivers of phytoplankton phenology off SW Iberia: A phenoregion based perspective

“…The accuracy of satellite-derived Chl-a for the study region and adjacent Atlantic areas was previously assessed during calibration-validation exercises dedicated to different (standard and alternative) bio-optical algorithms and sensors, including SeaWiFS (Navarro and Ruiz, 2006), MERIS (Cristina et al, 2014(Cristina et al, , 2015(Cristina et al, , 2016a(Cristina et al, , 2016bNechad et al, 2015;Goela et al, 2016b;Tilstone et al, 2017), MODIS-Aqua (Caballero et al, 2014;Sá et al, 2015) and OC-CCI version 1 products (Sá et al, 2015). Overall, these studies indicated that satellite radiometry provides realistic estimates of in situ Chl-a, but usually larger than contemporaneous in situ estimates.…”

A high‐performance computing framework for Monte Carlo ocean color simulations

“…MLP neural networks have been widely used for a number of applications including performance modeling , job scheduling , load balancing , design space exploration , and OC bio‐optical inversion . A common finding among these studies is that a key to successful MLP applications is empirical tuning of MLP algorithms in terms of network architecture, data pre‐processing, and application‐specific feature selection.…”