Informing ocean color inversion products by seeding with ancillary observations.

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data ('seeding') within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor.

Seasonal bias in global ocean color observations.

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (Rrs(λ);sr-1) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs(λ), and we recommend more simulations to discern these influence Rrs(λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

Particulate Backscattering in the Global Ocean: A Comparison of Independent Assessments.

How well do we know the particulate backscattering coefficient (bbp) in the global ocean? Satellite lidar bbp has never been validated globally and few studies have compared lidar bbp to bbp derived from reflectances (via ocean color) or in situ observations. Here, we validate lidar bbp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color bbp at the same locations and times to assess performance. Lidar bbp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ±50%.

Under the hood of satellite empirical chlorophyll a algorithms: revealing the dependencies of maximum band ratio algorithms on inherent optical properties.

Empirically-based satellite estimates of chlorophyll a [Chl] (e.g. OC3) are an important indicator of phytoplankton biomass. To correctly interpret [Chl] variability, estimates must be accurate and sources of algorithm errors known. While the underlying assumptions of band ratio algorithms such as OC3 have been tacitly hypothesized (i.e. CDOM and phytoplankton absorption covary), the influence of component absorption and scattering on the shape of the algorithm and estimated [Chl] error has yet to be explicitly revealed. We utilized the NOMAD bio-optical data set to examine variations between satellite estimated [Chl] and in situ values. We partitioned the variability into (a) signal contamination and (b) natural phytoplankton variability (variability in chlorophyll-specific phytoplankton absorption). Not surprisingly, the OC3 best-fit curve resulted from a balance between these two different sources of variation confirming the bias by detrital absorption on global scale. Unlike previous descriptions of empirical [Chl] algorithms, our study (a) quantified the mean detrital:phytoplankton absorption as ~1:1in the global NOMAD data set, and (b) removed detrital (CDOM + non-algal particle) absorption in radiative transfer models directly showing that the scale of the remaining variability in the band ratio algorithm was dominated by phytoplankton absorption cross section.

Calibration of SeaWiFS. II. Vicarious techniques.

We present an overview of the vicarious calibration of the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS). This program has three components: the calibration of the near-infrared bands so that the atmospheric correction algorithm retrieves the optical properties of maritime aerosols in the open ocean; the calibration of the visible bands against in-water measurements from the Marine Optical Buoy (MOBY); and a calibration-verification program that uses comparisons between SeaWiFS retrievals and globally distributed in situ measurements of water-leaving radiances. This paper describes the procedures as implemented for the third reprocessing of the SeaWiFS global mission data set. The uncertainty in the near-infrared vicarious gain is 0.9%. The uncertainties in the visible-band vicarious gains are 0.3%, corresponding to uncertainties in the water-leaving radiances of approximately 3%. The means of the SeaWiFS/in situ matchup ratios for water-leaving radiances are typically within 5% of unity in Case 1 waters, while chlorophyll a ratios are within 1% of unity. SeaWiFS is the first ocean-color mission to use an extensive and ongoing prelaunch and postlaunch calibration program, and the matchup results demonstrate the benefits of a comprehensive approach.