Adam Lawson

The southward propagation of cold-air frontal boundaries into the Gulf of Mexico region initiates a cascade of coupled air–sea processes that manifests along the coastlines as an apparent brightness anomaly in the ocean color signals. Our hypothesis is that the color anomaly is largely due to the turbulent resuspension of sedimentary particles. Initially, there is significant wind-driven ocean turbulence as the frontal boundary passes, followed by the potential for sustained convective instability due to significant heat losses from the ocean surface. These cold front events occur during boreal autumn, winter, and into early spring, and the latter episodes occur in the context of the seasonally recurring thermal stratification of shelf waters. Here, we show that as stratification reasserts thermal stability in the waning days of a cold front episode, daily to hourly ocean color patterns are temporally coherent with the air–sea heat flux changes and the resulting impact on water column stability. Concomitant results from a nested, data-assimilative, and two-way coupled ocean-atmosphere numerical modeling system provides both corroboration and insight into how surface air–sea fluxes and in-water turbulent mixing manifest as hourly changes in apparent surface water turbidity due to the potential excitation and settling of reflective particles. A simple model of particle mixing and settling driven by the simulated turbulence mimics patterns seen in the satellite image sequences. This study offers a preview of potential application areas that may emerge following the launch of a dedicated ocean color geostationary sensor.

Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [L_w(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between L_w and top-of-atmosphere (L_t)/Rayleigh (L_r) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams.

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors.

Atmospheric cold front propagation across the northern Gulf of Mexico is characterized by elevated surface wind velocities and a ~10–15 °C drop in surface air temperatures. These meteorological conditions result in significant heat energy losses from the surface ocean to the overlying atmosphere. These seasonally recurring cold-air outbreak events may penetrate the southern portion of the West Florida continental shelf and initiate turbulent and convective overturn of the water column. Examination of true color images derived from ocean-viewing, satellite-based radiometer data reveals coincident and substantial surface water discolorations that are optically similar to smaller-scale “whiting events,” despite the regional-scale extent of the observed phenomenon (>25,000 km²). Coupled air–sea numerical simulations suggest the surface water discoloration occurs and is sustained where the entire water column is dynamically unstable. The simulation results indicate significant density (σ_t) inversions between the surface and bottom waters. Thus, the combined numerical model and remote sensing analysis suggest that convective turbulence may be contributing to the sustained ventilation of bottom waters containing a high concentration of suspended particulates. High-temporal resolution true color images rendered from the GOES-R Advanced Baseline Imager (ABI) data appear to support the surface water discoloration’s turbulent-driven nature.

The combination of increased spectral resolution for in situ ocean optical instrumentation as well as future ocean remote sensing missions (e.g., PACE) provides an opportunity to examine new methods of analysis and ocean monitoring that were not feasible during the multispectral satellite era. For example, hyperspectral data enables a much more precise determination of the apparent true color for natural waters, one based on the full spectral shape of water-leaving radiance distributions. Herein we provide examples of how specific integrated biogeo-optical and physical processes in the northern Gulf of Mexico have characteristic hyperspectral signatures, and thusly, characteristic true color identifiers. Our emergent hypothesis is that once the characteristic hyperspectral color signature of a specific biophysical process is known, it can be detected and monitored even with multispectral or broad-band response digital imaging systems. To test this hypothesis, we examine archived imagery from MODIS and HICO to identify putative bottom boundary layer ventilation events along divergent shelf-frontal boundaries across the northern Gulf continental margin. Whereas on-demand in situ physical data that provide spatiotemporal correspondence with archived images are not available, we employ the data-assimilative Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) as a physical data surrogate. Preliminary results of this method appear to support the hypothesis, with the caveat that model results must be interpreted with due caution.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance (nLw) products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the nLw measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed correlates well within the same image slot where the coefficient of determination (r²) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 [mW/(cm²srμm)] at 555 nm to 0.52 [mW/(cm²srμm)] at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 [mW/(cm²srμm)] at 443 nm to 0.69 [mW/(cm²srμm)] at 490 nm.

Work performed under the Space and Naval Warfare Systems Center (SPAWARSYSCEN) Preparing Tactical Ocean Optical Products from Future Polar-Orbiting Sensors project enables exploitation of the Visible Infrared Imager Radiometer Suite (VIIRS) and similar ocean color sensors to provide the Navy with ocean color products to support operations. Work completed was in response to the Navy's need to exploit all available remote sensing technologies as part of a larger effort to provide a continuous operational picture of environmental conditions of the battlespace. Automated Optical Processing System (AOPS) version 4.12 provides tools and algorithms to process data from environmental remote sensing satellites and enables rapid near-real-time dissemination of final products. This Validation Test Report (VTR) provides the technical basis to transition the AOPS v4.12 to the NP3 Ocean Optics branch of the Naval Oceanographic Office (NAVOCEANO). AOPS is the software that allows NAVOCEANO to determine operational capability under various ocean optical conditions from satellite imagery. AOPS is used by NAVOCEANO to support fleet operators engaged in Naval Special Warfare (NSW), Mine Warfare (MIW), Expeditionary Warfare (EXW), and Anti-Submarine Warfare (ASW). Additionally, ocean color products are used in real time to support analysis of ocean models by oceanographers on the NAVOCEANO watch floor.

Since the October 2011 launch of the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument, a number of inter-sensor comparisons between VIIRS and MODIS (Moderate Resolution Imaging Spectroradiometer) radiances have been reported. Most of these comparisons are between calibrated radiances and temperatures based on observations of the two sensors from simultaneous nadir overpasses (SNO). Few comparisons between the retrieved ocean color data products, such as chlorophyll concentration, from VIIRS and MODIS data have been reported. Retrievals from measured data at large solar zenith angles and large view zenith angles are excluded from these comparison studies. In this paper, we report the inter-sensor comparisons between VIIRS and MODIS data acquired over the Chesapeake Bay and nearby areas with relatively large differences in sensor view angles. The goal for this study is to check the consistency between MODIS and VIIRS ocean color data products in order to merge the products from the two sensors. We compare total radiances (Lt) at the top of atmosphere (TOA) and the ocean color (OC) data products derived with the automatic processing system (APS) from both VIIRS and MODIS data. APS was developed at the Naval Research Laboratory, Stennis Space Center (NRL/SSC). We have found that, although there are large differences between the measured radiances (Lt) of the two sensors when the sensor zenith angle differences are significant, the mean percent differences between the retrieved normalized water-leaving radiances are about 15%. The results show that the variation in satellite view zenith angles is not a main factor affecting the retrieval of ocean color data products, i.e., the atmospheric correction routine adequately removes the view-angle dependence.

This document describes the testing and evaluation of the Automated Optical Processing System (AOPS) version 4.8 for the Visible Infrared Imager Radiometer Suite (VIIRS) sensor. AOPS enables exploitation of multiple space-borne ocean color satellite sensors to provide optical conditions for operational Navy products supporting mine Warfare (MIW), Naval Special Warfare (NSW), expeditionary warfare (EXW), and antisubmarine warfare (ASW). Ocean optical products are used to predict the impact of the environment on diver operations, communications, mine detection, and target detection. As part of this evaluation, comparisons are made with existing operational satellite products from MOSIA- Aqua, in situ data from ocean cruises and comparison with the Aerosol Robotic Network-Ocean Color (AERONET-OC) SeaPrism sensors. These comparisons show that VIIRS generates high quality ocean color products and should provide a continued data stream to support operational products