Development of a bio-optical model for the Barents Sea to quantitatively link glider and satellite observations

Abstract

A bio-optical model for the Barents Sea is determined from a set of in situ observations of inherent optical properties (IOPs) and associated biogeochemical analyses. The bio-optical model provides a pathway to convert commonly measured parameters from glider-borne sensors (CTD, optical triplet sensor—chlorophyll and CDOM fluorescence, backscattering coefficients) to bulk spectral IOPs (absorption, attenuation and backscattering). IOPs derived from glider observations are subsequently used to estimate remote sensing reflectance spectra that compare well with coincident satellite observations, providing independent validation of the general applicability of the bio-optical model. Various challenges in the generation of a robust bio-optical model involving dealing with partial and limited quantity datasets and the interpretation of data from the optical triplet sensor are discussed. Establishing this quantitative link between glider-borne and satellite-borne data sources is an important step in integrating these data streams and has wide applicability for current and future integrated autonomous observation systems. This article is part of the theme issue ‘The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning’.

The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning

“…They conclude that the uneven distribution and sparsity of chlorophyll measurements in the Arctic Ocean mean that the common practice of spatial and temporal averaging of profile data underestimates the importance of subsurface chlorophyll maxima. Next, Kostakis et al [24] study a multitude of biogeochemical parameters in the Barents Sea water column under different ice conditions using a glider system, a technology capable of covering wide areas of the ocean autonomously and complementary to satellite-derived data. Using these data, they develop and test a bio-optical model that links commonly measured parameters from glider-mounted sensors with satellite-derived measurements of bulk optical properties.…”

“…Next, Kostakis et al . [24] study a multitude of biogeochemical parameters in the Barents Sea water column under different ice conditions using a glider system, a technology capable of covering wide areas of the ocean autonomously and complementary to satellite-derived data. Using these data, they develop and test a bio-optical model that links commonly measured parameters from glider-mounted sensors with satellite-derived measurements of bulk optical properties.…”

Improving Chlorophyll-A Estimation From Sentinel-2 (MSI) in the Barents Sea Using Machine Learning

“…Several studies have been conducted on modeling the net primary production and Chl-a content in the Barents Sea, though, many are solely based on in-situ measurements [6], [11]- [15]. Several methods integrating in-situ with satellite based observations have also been proposed [1], [16]- [23]. These studies on Chl-a retrieval are either based on empirical or semi-analytical approaches and confined to relatively small spatial and temporal scales.…”

“…These studies on Chl-a retrieval are either based on empirical or semi-analytical approaches and confined to relatively small spatial and temporal scales. Some of the existing methods are applied to in-situ remote sensing reflectance (R r s ) data and validated on either low spatial resolution satellite sensors or limited to validation on a few images [23]. For example, Le et al [1] used a three-dimensional seaice plankton ecosystem model to study primary production in the northern Barents Sea for only summer months.…”

“…More recently, a bio-optical model was developed from a set of in-situ observations of Chl-a and Inherit Optical Properties (IOP's) collected only in the bloom season. Due to cloud cover and longer time-gaps, the estimated R r s spectra derived from IOP's were validated with an 8-day average MODIS-A observation [23]. Thus, most of the existing methods are not validated independently on high resolution satellite data such as Sentinel-2 Multi Spectral Instrument (MSI) covering a wide area of Barents Sea.…”