The Naval Research Laboratory (NRL) has established a Regional Coastal Oceanography with Nanosatellites (ReCON) project which will explore the ability of high-resolution nanosatellites to monitor coastal, estuarine, riverine, and other maritime environments in support of U.S. Navy operations. The project will initially focus on using data from the almost 150+ Planet “Dove” nanosatellites which fly in “flocks” acquiring remotely sensed data from sunlight reflecting off the earth surface. The usefulness of remotely sensed data within our research and operations is determined by the ability to accurately perform atmospheric correction and compute water leaving radiances (Lw), which are then normalized (nLw) and form the basis for the generation of remote sensing reflectance and other inherent and apparent optical property products. These nanosatellites have a single infrared band, although two such bands are typically required to automatically select an appropriate aerosol model during atmospheric correction, prior to estimating nLw. While early in the project, this initial study will assess nanosatellite capabilities to accurately retrieve nLw measurements by specifying the aerosol model selection during the atmospheric correction process. Here we present nLw retrievals for a variety of Planet nanosatellite imagery covering an entire year over a northern island of Venezuela, which covers coastal and open ocean type waters. The nLw retrievals from the nanosatellites using forced aerosol models are compared to coincident nLw retrievals from the Suomi-National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to gauge the potential reliability and accuracy of using nanosatellite imagery as a competent data source for ocean color optics.
The SAtellite VAlidation Navy Tool (SAVANT) was developed by the Navy to help facilitate the assessment of the stability and accuracy of ocean color satellites using ground truth (insitu) platform and buoy stations positioned around the globe and support methods for match-up protocols. This automated, continuous monitoring system for satellite ocean color sensors employs a website interface to extract and graph coincident satellite and insitu data in near-real-time. Available satellite sensors include MODerate resolution Imaging Spectrometer (MODIS) onboard the Aqua satellite, Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbitting Partnership (SNPP) & Joint Polar Satellite Sensor (JPSS), Ocean and Land Colour Instrument (OLCI) onboard the Sentinel 3A and Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean and Meteorological Satellite (COMS). SAVANT houses an extensive match-up data set covering nineteen plus years (2000- 2019) of coincident global satellite and ground truth spectral Normalized Water Leaving Radiance (nLw) data allowing users to evaluate the accuracy of ocean color sensors spectral water leaving radiance at specific ground truth sites that provide continuous data. The tool permits changing different match-up constraints and evaluating the effects on the match-up uncertainty. Results include: a) the effects of spatial selection (using single satellite pixel versus 3x3 and 5x5 boxes, all centered around the insitu location), b) time difference between satellite overpass and ground truth observations, c) and satellite and solar zenith angles. Match-up uncertainty analyses was performed on VIIRS SNPP at the AErosol RObotic NETwork Ocean Color (AeroNET-OC) Wave Current surge Information System (WavCIS) site, maintained by NRL and the Louisiana State University (LSU) in the North Central Gulf of Mexico onboard the Chevron platform CSI-06. The VIIRS SNPP and AeroNET-OC assessment determined optimal satellite ocean color cal/val match-up protocols that reduced uncertainty in the derived satellite products.
Herein we present an initial approach for assessing water color, specifically chromaticity, and determining if an accurate correlation can be made within chromaticity space between the water color and a hyperspectral synthetic data set. The water color assessed in this paper consist of remote sensing reflectance (Rrs) distributions from the Suomi-National Polarorbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and the hyperspectral synthetic data set consists of Rrs distributions of natural marine waters. Where strong correlations exist, the hyperspectral Rrs reference data can be blended into the SNPP VIIRS Rrs data, thus creating a hyperspectral SNPP VIIRS spectra. Where applicable, the newly constructed VIIRS hyperspectral signature is compared to in situ data taken during a 2018 National Oceanic and Atmospheric Administration (NOAA) Calibration/Validation cruise. Given the proliferation of small, low-cost airborne platforms equipped with color imaging cameras, there exists tremendous potential to use and hyperspectrally enhance these data streams for ocean monitoring and scientific research. However, techniques for extracting traditional ocean radiant spectra from RGB data fields are new to oceanographic disciplines.
Boreal winter meteorological fronts manifest across the northern Gulf of Mexico as rapid 10-15° C drops in air temperature and accelerating northerly winds. The physical coastal ocean response across the Louisiana-Texas (LATEX) continental shelf system involves a complex interplay between coastal buoyancy, wind forcing, and intense thermal energy fluxes out of the ocean. Herein we combine numerical simulations, in situ optical surveys, and coincident satellite images derived from the Ocean and Land Colour Imager (OLCI) and other sensors to further unravel the mechanistic functioning and optical signatures of these complex events. The conspicuous optical gradients evident in color satellite images coincident with cold air outbreak (CAO) events appear to result from surface ventilation of sediment-laden bottom waters and wind/buoyancy-driven surface currents. The hyperspectral gradients associated with water mass types (sediment resuspension in marine waters versus freshwater effluent plumes) give rise to true color gradients that may be tracked with low spectral resolution color sensors at very high temporal resolution.
In September, 2018, Hurricane Florence made landfall in North Carolina as a Category 1 hurricane and inundated the eastern United States with significant rainfall. Precipitation from this slow moving storm event caused massive flooding. Outflow from this flooding carried suspended solids including sediments and other particulates as the rainwater worked its way through river and watershed systems toward the Atlantic Ocean. The Advanced Baseline Imager (ABI) on the NOAA Geostationary Operational Environmental Satellite - 16 (GOES-16) monitors the eastern United States. ABI data from GOES-16 is available every 5 minutes and provides a platform for studying the increased volume of river flow into the Atlantic Ocean. Data from the GOES-16 ABI covering the Atlantic waters off the eastern United States were downloaded after the Hurricane Florence event. Methodologies for atmospheric correction were used to generate water leaving radiance values from the GOES-16 ABI data sets. Using the multiple looks per day, the plumes of suspended solids were delineated and studied.
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.
Standard oceanographic processing of the visible infrared imaging radiometer suite (VIIRS) and the moderate resolution imaging spectroradiometer (MODIS) data uses established atmospheric correction approaches to generate normalized water-leaving radiances (nLw) and bio-optical products. In many cases, there are minimal differences between temporally and spatially coincident MODIS and VIIRS bio-optical products. However, due to factors such as atmospheric effects, sensor, and solar geometry differences, there are cases where the sensors’ derived products do not compare favorably. When these cases occur, selected nLw values from one sensor can be used to vicariously calibrate the other sensor. Coincident VIIRS and MODIS scenes were used to test this cross-sensor calibration method. The VIIRS sensor was selected as the “base” sensor providing “synthetic” in situnLw data for vicarious calibration, which computed new sensor gain factors used to reprocess the coincident MODIS scene. This reduced the differences between the VIIRS and MODIS bio-optical measurement. Chlorophyll products from standard and cross-sensor calibrated MODIS scenes were fused with the VIIRS chlorophyll product to demonstrate the ability for this cross-sensor calibration and product fusion method to remove atmospheric and cloud features. This cross-sensor calibration method can be extended to other current and future sensors.
The Hyperspectral Imager for the Coastal Ocean (HICO) is a prototype sensor installed on the International Space Station (ISS) designed to explore the management and capability of a space-borne hyperspectral sensor. The Office of Naval Research (ONR) funded the development and management of HICO. The Naval Research Laboratory (NRL) built and is involved in management of the HICO sensor. Bathymetry information is essential for naval operations in coastal regions. However, bathymetry may not be available in denied areas. HICO has a 100 meter spatial resolution, which makes it more capable for providing information within bays and estuaries than other sensors with coarser resolutions. Furthermore, its contiguous hyperspectral range is well suited to be used as input to the Hyperspectral Optimization Process Exemplar (HOPE) algorithm, which along with other absorption and backscattering values, estimates bottom albedo and water depth. Vicarious calibration uses in situ data to generate new gains and offsets that when applied to the top-of-atmosphere radiance values improves atmospheric correction results and the measurement of normalized water-leaving radiances. In situ remote sensing reflectance data collected in St. Andrews Bay were used to vicariously calibrate a coincident HICO scene. NRL’s Automated Processing System (APS) was used to perform atmospheric correction and estimation of remote sensing reflectance (Rrs). The HOPE algorithm used the vicariously calibrated HICO Rrs values to estimate water depth. The results were validated with bathymetry maps from the NOAA National Ocean Service (NOS).
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