An Inherent Optical Properties Data Processing System for Achieving Consistent Ocean Color Products From Different Ocean Color Satellites

We used field measurements and multimission satellite data to evaluate how well an inherent optical properties (IOPs) data processing system performed at correcting the residual error of the atmospheric correction in satellite remote sensing reflectance (R-rs) and how well the system simultaneously minimized intermission biases between different remote sensing systems. We developed the IOPs data processing system as a semianalytical algorithm called IDAS. Our results show that IDAS generates accurate and consistent IOPs products from two ocean color missions: Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer Aqua (MODISA). Specifically, with "high-quality" SeaWiFS and MODISA R-rs data, IDAS provided temporally consistent IOPs products for the oligotrophic open ocean resulting in an annual mean intermission difference of less than 3%, which is significantly lower than what a quasi-analytical algorithm (QAA) provided. We used IDAS to generate a long time series of b(b)(555) from the Northwest Atlantic Subtropical Gyre using SeaWiFS (1998 to 2002) and MODISA (2003 to 2017) images. Our results show that the IDAS-derived annual b(b)(555) decreased monotonically by 2.81% per decade from 1998 to 2017. Comparing the IDAS-generated annual trend for b(b)(555) to the same data processed with the QAA algorithm, we found that the QAA results differed because of impacts of the residual errors of the atmospheric correction and intermission biases. The differences in the annual trends existed despite the same temporal changing patterns of in situ particulate organic carbon existing in the Sargasso Sea and in the satellite chlorophyll-a concentration in the Northwest Atlantic Subtropical Gyre

Evaluation of the CALIPSO Lidar-observed particulate backscattering coefficient on different spatiotemporal matchup scales

The satellite lidar-derived ocean particulate backscattering coefficient (bbp) has rarely been validated globally with in situ observations, and we need to understand how well the satellite CALIPSO lidar bbp approach performs. Whether lidar bbp performs better in terms of observation accuracy compared to passive ocean color remote sensing has yet to be evaluated for detailed validation. With the continued deployment of the BGC-Argo float array in the global open ocean in recent years, data have accumulated with a total of 42,932 particulate backscattering coefficients (bbp) from 2010 to 2017, allowing for a finer spatial and temporal scale evaluation of the performance of the CALIPSO lidar-observed bbp. We evaluated the performance of CALIPSO-retrieved bbp products using the data detected by the BGC-Argo floats at 12 spatiotemporal matchup scales and discussed the differences in product performance at various interannual, seasonal, and spatial scales. We compare lidar, float, and ocean color bbp at the same locations and times and find that lidar bbp outperforms ocean color data. We also analyzed the key conversion factor β(π)/bbp at different spatial and temporal scales and found that there was a seasonal difference in the optimal conversion factor

Persistent and Progressive Outer Retina Thinning in Frontotemporal Degeneration

ObjectiveWhile Alzheimer’s disease is associated with inner retina thinning measured by spectral-domain optical coherence tomography (SD-OCT), our previous cross-sectional study suggested outer retina thinning in frontotemporal degeneration (FTD) patients compared to controls without neurodegenerative disease; we sought to evaluate longitudinal changes of this potential biomarker.MethodsSD-OCT retinal layer thicknesses were measured at baseline and after 1–2 years. Clinical criteria, genetic analysis, and a cerebrospinal fluid biomarker (total tau: β-amyloid) to exclude likely underlying Alzheimer’s disease pathology were used to define a subgroup of predicted molecular pathology (i.e., tauopathy). Retinal layer thicknesses and rates of change in all FTD patients (n = 16 patients, 30 eyes) and the tauopathy subgroup (n = 9 patients,16 eyes) were compared to controls (n = 30 controls, 47 eyes) using a generalized linear model accounting for inter-eye correlation and adjusting for age, sex, and race. Correlations between retinal layer thicknesses and Mini-Mental State Examinations (MMSE) were assessed.ResultsCompared to controls, returning FTD patients (143 vs. 130 μm, p = 0.005) and the tauopathy subgroup (143 vs. 128 μm, p = 0.03) had thinner outer retinas but similar inner layer thicknesses. Compared to controls, the outer retina thinning rate was not significant for all FTD patients (p = 0.34), but was significant for the tauopathy subgroup (−3.9 vs. 0.4 μm/year, p = 0.03). Outer retina thickness change correlated with MMSE change in FTD patients (Spearman rho = 0.60, p = 0.02) and the tauopathy subgroup (rho = 0.73, p = 0.04).ConclusionOur finding of FTD outer retina thinning persists and longitudinally correlates with disease progression. These findings were especially seen in probable tauopathy patients, which showed progressive outer retina thinning

An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas

Validation of S-NPP VIIRS Sea Surface Temperature Retrieved from NAVO

The validation of sea surface temperature (SST) retrieved from the new sensor Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite is essential for the interpretation, use, and improvement of the new generation SST product. In this study, the magnitude and characteristics of uncertainties in S-NPP VIIRS SST produced by the Naval Oceanographic Office (NAVO) are investigated. The NAVO S-NPP VIIRS SST and eight types of quality-controlled in situ SST from the National Oceanic and Atmospheric Administration in situ Quality Monitor (iQuam) are condensed into a Taylor diagram. Considering these comparisons and their spatial coverage, the NAVO S-NPP VIIRS SST is then validated using collocated drifters measured SST via a three-way error analysis which also includes SST derived from Moderate Resolution Imaging Spectro-radiometer (MODIS) onboard AQUA. The analysis shows that the NAVO S-NPP VIIRS SST is of high accuracy, which lies between the drifters measured SST and AQUA MODIS SST. The histogram of NAVO S-NPP VIIRS SST root-mean-square error (RMSE) shows normality in the range of 0–0.6 °C with a median of ~0.31 °C. Global distribution of NAVO VIIRS SST shows pronounced warm biases up to 0.5 °C in the Southern Hemisphere at high latitudes with respect to the drifters measured SST, while near-zero biases are observed in AQUA MODIS. It means that these biases may be caused by the NAVO S-NPP VIIRS SST retrieval algorithm rather than the nature of the SST. The reasons and correction for this bias need to be further studied

Mass Deposition Fluxes of Asian Dust to the Bohai Sea and Yellow Sea from Geostationary Satellite MTSAT: A Case Study

Windblown dust aerosol plays an important role in marine ecosystems once they are deposited and dissolved. At present, methods for estimating the deposition flux are mainly limited to direct measurements or model outputs. Additionally, satellite remote sensing was often used to estimate the integral dust column concentration (DCC). In this paper, an algorithm is developed to estimate the mass deposition fluxes of Asian dust by satellite. The dust aerosol is identified firstly and then the DCC is derived based on the relationships between the pre-calculated lookup table (LUT) and observations from Japanese geostationary Multi-functional Transport Satellites (MTSAT). The LUT is built on the dust cloud and surface parameters by a radiation transfer model Streamer. The average change rate of deposition is derived, which shows an exponential decay dependence on transport time along the pathway. Thus, the deposition flux is acquired via integrating the hourly deposition. This simple algorithm is applied to a dust storm that occurred in the Bohai Sea and Yellow Sea from 1 to 3 March 2008. Results indicate that the properties of the dust cloud over the study area changed rapidly and the mass deposition flux is estimated to be 2.59 Mt

A New Semi-Analytical MC Model for Oceanic LIDAR Inelastic Signals

The design and processing algorithm of oceanic LIDAR requires an effective lidar simulator. Currently, most simulation methods for lidar signal propagation in seawater use elastic scattering. In this study, a new semi-analytical Monte Carlo (MC) model for oceanic lidar inelastic signals is developed to investigate chlorophyll fluorescence and Raman scattering in seawater. We also used this model to simulate the echo signal of high spectral resolution lidar (HSRL) in the particulate and water molecular channels. Using this model, the effects of chlorophyll concentration, multiple scattering, receiving field of view (FOV), scattering phase function (SPF), receiver full width at half maximum (FWHM) and inhomogeneous seawater were investigated. The feasibility and effectiveness of the model were verified by the lidar equation under small and large FOVs. The results showed that chlorophyll concentration and vertical structure and multiple scattering have considerable and integrated effects on echo signals, which could provide a reference for the design of oceanic fluorescence and HSRL lidar systems and contribute to the development of processing algorithms