Paul Martinolich

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.

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.

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