Generalized ocean color inversion model for retrieving marine inherent optical properties

Abstract

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

“…Twelve models required satellite products (see Table 3) other than surface chlorophyll and PAR, such as remote sensing reflectance ( Rrs ) at 6 bands (412, 443, 490, 510, 555, and 670 nm from SeaWiFS, and 412, 443, 488, 531, 555, and 667 from MODIS‐Aqua), phytoplankton absorption coefficient ( aph ) derived by the generalized inherent optical property ( GIOP ) algorithm [ Werdell et al ., 2013] at 5 bands (412, 443, 490, 510, and 555 nm from SeaWiFS, and 412, 443, 488, 531, and 547 from MODIS‐Aqua), quasi‐analytical algorithm ( QAA ) total absorption coefficient at 443nm ( a443 ) [ Lee et al ., 2002], GIOP particulate backscattering coefficient at 443 nm ( bbp443 ), and the Lee euphotic depth product ( Z eu_lee ) [ Lee et al ., 2007]. Like satellite chlorophyll and PAR, similar procedures were performed to extract values at the satellite matchup stations using the level‐3, 9 km, 8 day average imagery from SeaWiFS (N=13 from November 1997 to June 2002) and MODIS‐Aqua (N=72 from July 2002 to October 2011).…”

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

“…Regarding optically shallow waters, these can be characterized as zones in which light reflected from the seafloor influences the waterleaving radiance signal [Lee et al, 1998] thereby confounding contemporary ocean color algorithms developed for optically deep waters [Cannizzaro and Carder, 2006;Qin et al, 2007;Zhao et al, 2013] (see Appendix A for further discussion). Whilst a range of ocean color algorithms have been developed and proven effective within optically complex waters [Doerffer and Schiller, 2007;Smyth et al, 2006;Werdell et al, 2013a], only a few approaches for optically shallow waters have been published [Barnes et al, 2014Brando et al, 2012] with none in operation that explicitly use pre-existing water column depth and benthic albedo data sets to improve IOP retrievals.…”

“…We have selected the Great Barrier Reef (GBR) as the test region for algorithm development and evaluation as the bathymetry, and the benthic properties of this shallow shelf region are well characterized. Within this paper, we: (i) detail the structure of the SWIM algorithm, (ii) present a brief overview of algorithm performance based on radiative transfer modeling, (iii) demonstrate how the inclusion of depth and benthic albedo influences IOP retrievals in a MODIS Aqua test scene, (iv) using the full MODIS Aqua archive, compare SWIM-derived IOPs to those derived using the Generalized IOP (GIOP) [Werdell et al, 2013a] and Quasi-Analytical Algorithm (QAA) optically deep ocean color algorithms, and (v) discuss the relative performance and limitations of the SWIM algorithm. Unfortunately, in situ IOP data for the GBR could not be sourced at the time of writing this paper.…”

Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES

“…Semi-analytical algorithms have been developed which, in addition to empirical input, use radiative-transfer theory to invert optical constituents in the open ocean. These algorithms, most often, obtain the combined absorption of non-phytoplankton and dissolved material, phytoplankton absorption, the associated chlorophyll concentration, and the backscattering coefficient (see IOCCG, 2006;Werdell et al, 2013). Semi-analytical algorithms provide information regarding size (Loisel et al, 2006;Kostadinov et al, 2009;Berwin et al, 2011) and phytoplankton composition as well (Kostadinov et al, 2010).…”