Sonia C. Gallegos

Holographic interferometry of oil films and droplets in water with a single-beam mirror-type scheme

Application of single-beam reflective laser optical interferometry for oil films and droplets in water detection and characterization is discussed. Oil films can be detected by the appearance of characteristic interference patterns. Analytical expressions describing intensity distribution in these interference patterns allow determination of oil film thickness, size of oil droplets, and distance to the oil film from the observation plane. Results from these analyses indicate that oil spill aging and breakup can be monitored in real time by analyzing time-dependent holographic fringe patterns. Interferometric methods of oil spill detection and characterization can be automated using digital holography with three-dimensional reconstruction of the time-changing oil spill topography. In this effort, the interferometric methods were applied to samples from Chevron oil and British Petroleum MC252 oil obtained during the Deep Water Horizon oil spill in the Gulf of Mexico.

Improving remotely sensed fused ocean data products through cross-sensor calibration

Abstract. 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 situ nLw 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.

Remote sensing and characterization of oil on water using coherent fringe projection and holographic in-line interferometry

Abstract. We suggest combining several optical methods for remote sensing and characterization of crude oil films and emulsions. These are coherent fringe projection illumination (CFP), holographic in-line interferometry (HILI), and laser-induced fluorescence. The combined methods of CFP and HILI are described in the frame of coherent superposition of partial interference patterns. It is shown that in addition to detection and identification of oil, laser illumination in the green-blue region can also degrade oil. This finding indicates that properly structured laser clean-up can be an alternative method of decontamination.

Comparative Analysis of GOCI Ocean Color Products

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 nLw 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.

Inter-Comparison between VIIRS and MODIS Radiances and Ocean Color Data Products over the Chesapeake Bay

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.

The Federal Oil Spill Team for Emergency Response Remote Sensing, FOSTERRS: Enabling Remote Sensing Technology for Marine Disaster Response

Oil spills cause significant to devastating ecological, economic, and societal damage, requiring years to decades for recovery. In cases of floods such as those associated with Hurricane Katrina, both oil and marine debris can enter the ocean, with debris posing its own hazards and ecological damage. In other cases, massive debris introduction can occur from natural causes such as the great Japanese tsunami in 2011.

Crude oil remote sensing, characterization, and cleaning with continuous wave and pulsed lasers

We demonstrate a successful combination of several optical methods of remote sensing (coherent fringe projection illumination (CFP), holographic in-line interferometry (HILI), laser induced fluorescence,) for detection, identification, and characterization of crude oil. These methods enable the three-dimensional characterization of oil spills that is important for practical applications. Combined methods of CFP and HILI are described in the frame of coherent superposition of partial interference patterns. We show that in addition to detection/identification of oil, laser illumination in the green-blue region can also degrade oil slicks. We tested these methods on differentsurfaces contaminated by oil , which include: oil on water, oil on flat solid surfaces, and oil on curved surfaces of. We use coherent fiber bundles for the detection and monitoring of the laser-induced oil degradation in pipes.. Both continuouswave (CW) and pulsed lasers are tested using pump-probe schemes. This finding allows us to suggest that properly structured laser clean-up can be an alternative environmental-friendly method of decontamination and cleaning, which can be an alternative to chemical methods, which are dangerous to environmentApplication of holographic amplifier with phase conjugation will allow to increase sensitivity, reduce aberrations from atmospheric distortions and to focus back-reflected amplified beam on the contaminated area thus accelerating laser cleaning.

Evaluation of the Naval Research Laboratory algorithm for identifying clouds in ocean color data

The performance of an algorithm which identifies and removes clouds from ocean color satellite data over the ocean is evaluated. The evaluations were accomplished with archived sets of Coastal Zone Color Scanner (CZCS) and simulated SeaWiFS data. The simulated SeaWiFS sets were produced by spectrally and spatially convoluting the data of the Airborne Visible-Infrared Imaging Spectrometer to fit the spectral responses of the SeaWiFS channels and achieve the same spatial resolution of the SeaWiFS instrument. This study also included evaluations of the effect that changes in spatial resolution have on the texture computations of the algorithm. Convolution procedures were used to generate images at various spatial resolutions: 20 m, 50 m, 100 m, 500 m and 1 km. Results from the investigations involving CZCS Channel 5 (720 - 800 nm) data indicate that the algorithm performs well in all the oceanic data sets tested but not in the coastal imagery. Cloud masking in the coastal sets was clearly affected by bottom reflectances and river run off. In the evaluations involving simulated SeaWiFS Channel 8 (845 - 885 nm) data, the algorithm performed accurately every time and was not affected by coastal or oceanic features. It began to confuse cloud shadow edges with cloud edges at resolutions of 30 m or less. From the results obtained, it appears that this algorithm has the potential to identify clouds not only in SeaWiFS data, but also in data from high resolution color sensors.