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%.

Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats.

Satellite retrievals of particulate backscattering (bbp) are widely used in studies of ocean ecology and biogeochemistry, but have been historically difficult to validate due to the paucity of available ship-based comparative field measurements. Here we present a comparison of satellite and in situ bbp using observations from autonomous floats (n = 2,486 total matchups across three satellites), which provide bbp at 700 nm. With these data, we quantify how well the three inversion products currently distributed by NASA ocean color retrieve bbp. We find that the median ratio of satellite derived bbp to float bbp ranges from 0.77 to 1.60 and Spearman's rank correlations vary from r = 0.06 to r = 0.79, depending on which algorithm and sensor is used. Model skill degrades with increased spatial variability in remote sensing reflectance, which suggests that more rigorous matchup criteria and factors contributing to sensor noisiness may be useful to address in future work, and/or that we have built in biases in the current widely distributed inversion algorithms.