Validation and Intercomparison of Ocean Color Algorithms for Estimating Particulate Organic Carbon in the Oceans

Abstract

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-color signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean color data requires algorithms that are well validated, with uncertainties characterized. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean color data provided by the Ocean Color Climate Change Initiative (OC-CCI) and validated against the largest database of in situ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of Loisel et al., 2002;Stramski et al., 2008) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

“…The procedure for match-up selection was the same as that used for particulate organic carbon (POC) data (Evers-King et al, 2017). The day of year the in situ sample was collected was matched with the same day of year from the merged satellite products.…”

A Color‐Index‐Based Empirical Algorithm for Determining Particulate Organic Carbon Concentration in the Ocean From Satellite Observations

“…The empirical algorithms for estimating POC directly from the spectral remote‐sensing reflectance, R rs (λ), where λ is light wavelength, have also been developed and evaluated in the past (Allison et al, ; Stramska & Stramski, ; Stramski et al, ). Evaluation and comparisons of performance of different types of POC algorithms show that simple empirical algorithms based on the input data of blue‐to‐green (BG) band ratio of reflectance generally outperform two‐step algorithms in the applications on large ocean basin and global scales (Allison et al, ; Evers‐King et al, ; Stramska & Stramski, ). Currently, the National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group uses the BG band‐ratio algorithm to generate the standard global POC data product from ocean color data, and this product has been made available on the OB.DAAC website (http://oceancolor.gsfc.nasa.gov).…”

“…Recent evaluation of POC algorithms using a large satellite‐in situ matchup data set (3,891 matchups utilizing satellite data from SeaWiFS, MODIS/Aqua, and MERIS) indicated a reasonably good performance of the standard BG algorithm in open ocean waters at global scale with an overall median absolute percent difference of 25% (±37% interquartile range) between the satellite‐derived POC and in situ POC matchups (Evers‐King et al, ). In another recent study evaluating the standard BG algorithm with satellite‐in situ matchups of POC (satellite data from SeaWiFS and MODIS/Aqua), Świrgoń and Stramska () reported an overall mean absolute percent difference of 33% when GAC, MLAC, and LAC satellite data were included in the analysis of 260 matchups and 37% when only LAC data were included (99 matchups).…”

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

“…Many bio-optical algorithms have been developed to derive the near-surface concentration of POC from satellite measurements [7][8][9][10][11][12][13][14] and an inter-comparison exercise was recently performed to test their respective performances [17]. These algorithms were developed for open ocean waters and rely on the dominance of phytoplankton biomass in the total POC concentration.…”

“…These algorithms were developed for open waters and rely on the fact that the variability of the inherent optical properties is driven by phytoplankton and its associated material (heterotrophic bacteria, detritus, colored dissolved organic matter (CDOM)). The performance of different available POC algorithms for oceanic waters has recently been evaluated [17] showing that empirical approaches based on band ratios [11] and semi-analytical approaches based on the back scattering coefficient (b bp ) and chlorophyll-a concentration (Chla) [8] performed the best. While the application of these algorithms to OCR observations allowed the pool of POC over the open ocean to be estimated (about 0.4 and 1.2 Pg.…”