Refinement of the Critical Angle Calculation for the Contrast Reversal of Oil Slicks under Sunglint

It has long been observed that oil slicks under sunglint can reverse their optical contrast against nearby oil‐free seawater. Such a phenomenon has been described through both empirical statistical analysis of the sunglint strength and modeled theoretically using a critical angle concept. The critical angle, in this model, is the angle at which the image pixels show no or negligible contrast between oiled and nonoiled seawater. Pixels away from this critical angle show either positive or negative contrast from the oil‐free pixels. Although this concept has been fully demonstrated in the published literature, its calculation needs to be further refined to take into account: (1) the different refractive indices of oil slicks (from natural seeps) and seawater and (2) atmospheric effects in the sensor‐measured radiance. Using measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) over oil films in the Gulf of Mexico, we show improvement in the modeled and MODIS‐derived reflectance over oil slicks originated from natural seeps after incorporating these two factors in the model. Specifically, agreement between modeled and measured sunglint reflectance is found for both negative and positive‐contrasting oil slicks. These results indicate that surface roughness and reflectance from oil films can be estimated given any solar/viewing geometry and surface wind. Further, this model might be used to correct the sunglint effect on thick oil under similar illumination conditions. Once proven possible, it may allow existing laboratory‐based models, which estimate oil thickness after such corrections, to be applied to remote sensing imagery

Satellite-based Virtual Buoy System to Monitor Coastal Water Quality

There is a pressing need to assess coastal and estuarine water quality state and anomaly events to facilitate coastal management, but such a need is hindered by lack of resources to conduct frequent ship-based or buoy-based measurements. Here, we established a virtual buoy system (VBS) to facilitate satellite data visualization and interpretation of water quality assessment. The VBS is based on a virtual antenna system (VAS) that obtains low-level satellite data and generates higher-level data products using both National Aeronautics and Space Administration standard algorithms and regionally customized algorithms in near real time. The VB stations are predefined and carefully chosen to cover water quality gradients in estuaries and coastal waters, where multiyear time series at monthly and weekly intervals are extracted for the following parameters: sea surface temperature (°C), chlorophyll-a concentration (mg m−3 ), turbidity (NTU), diffuse light attenuation at 490 nm [Kd(490) , m−1 ] or secchi disk depth (m), absorption coefficient of colored dissolved organic matter (m−1 ), and bottom available light (%). The time-series data are updated routinely and provided in both ASCII and graphical formats via a user-friendly web interface where all information is available to the user through a simple click. The VAS and VBS also provide necessary infrastructure to implement peer-reviewed regional algorithms to generate and share improved water quality data products with the user community

Refinement of the Critical Angle Calculation for the Contrast Reversal of Oil Slicks under Sunglint

It has long been observed that oil slicks under sunglint can reverse their optical contrast against nearby oil‐free seawater. Such a phenomenon has been described through both empirical statistical analysis of the sunglint strength and modeled theoretically using a critical angle concept. The critical angle, in this model, is the angle at which the image pixels show no or negligible contrast between oiled and nonoiled seawater. Pixels away from this critical angle show either positive or negative contrast from the oil‐free pixels. Although this concept has been fully demonstrated in the published literature, its calculation needs to be further refined to take into account: (1) the different refractive indices of oil slicks (from natural seeps) and seawater and (2) atmospheric effects in the sensor‐measured radiance. Using measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) over oil films in the Gulf of Mexico, we show improvement in the modeled and MODIS‐derived reflectance over oil slicks originated from natural seeps after incorporating these two factors in the model. Specifically, agreement between modeled and measured sunglint reflectance is found for both negative and positive‐contrasting oil slicks. These results indicate that surface roughness and reflectance from oil films can be estimated given any solar/viewing geometry and surface wind. Further, this model might be used to correct the sunglint effect on thick oil under similar illumination conditions. Once proven possible, it may allow existing laboratory‐based models, which estimate oil thickness after such corrections, to be applied to remote sensing imagery

The Antares Observation Network

The ANTARES network seeks to understand the variability of the coastal environment on a continental scale and the local, regional, and global factors and processes that modulate this variability. The target are coastal zones of South America and the Caribbean Sea. The initial approach includes developing time series of in situ and satellite-based environmental observations in coastal and oceanic regions. The network is constituted by experts that seek to exchange ideas, develop an infrastructure for mutual logistical and knowledge support, and link in situ time series of observations located around the Americas with real-time and historical satellite-derived time series of relevant products. A major objective is to generate information that will be distributed publicly and openly in the service of coastal ocean research, resource management, science-based policy making and education in the Americas. As a first stage, the network has linked oceanographic time series located in Argentina, Brazil, Chile and Venezuela. The group has also developed an online tool to examine satellite data collected with sensors such as NASA\u27s MODIS. Specifically, continental-scale high-resolution (1 km) maps of chlorophyll and sea surface temperature are generated and served daily over the web according to specifications of users within the ANTARES network. Other satellite-derived variables will be added as support for the network is solidified. ANTARES makes data available and offers simple analysis tools that anyone can use with the ultimate goal of improving coastal assessments, management and policies

Satellite-based Virtual Buoy System to Monitor Coastal Water Quality

There is a pressing need to assess coastal and estuarine water quality state and anomaly events to facilitate coastal management, but such a need is hindered by lack of resources to conduct frequent ship-based or buoy-based measurements. Here, we established a virtual buoy system (VBS) to facilitate satellite data visualization and interpretation of water quality assessment. The VBS is based on a virtual antenna system (VAS) that obtains low-level satellite data and generates higher-level data products using both National Aeronautics and Space Administration standard algorithms and regionally customized algorithms in near real time. The VB stations are predefined and carefully chosen to cover water quality gradients in estuaries and coastal waters, where multiyear time series at monthly and weekly intervals are extracted for the following parameters: sea surface temperature (°C), chlorophyll-a concentration (mg m−3 ), turbidity (NTU), diffuse light attenuation at 490 nm [Kd(490) , m−1 ] or secchi disk depth (m), absorption coefficient of colored dissolved organic matter (m−1 ), and bottom available light (%). The time-series data are updated routinely and provided in both ASCII and graphical formats via a user-friendly web interface where all information is available to the user through a simple click. The VAS and VBS also provide necessary infrastructure to implement peer-reviewed regional algorithms to generate and share improved water quality data products with the user community

Detecting Surface Oil Slicks Using VIIRS Nighttime Imagery under Moon Glint: A Case Study in the Gulf of Mexico

Using data collected over the Gulf of Mexico during night between May 2012 and September 2013 by the Visible Infrared Imager Radiometer Suite (VIIRS), we demonstrate a new application from its day-and-night band (DNB). Under cloud free and moon glint conditions, the DNB revealed surface oil slicks from natural oil seeps. This is despite the fact that the signal-to-noise ratio (SNR) of this wide band (505–890 nm) under moon glint is much lower (30:1–50:1) and its resolution is also coarser (750 m) than the VIIRS imaging bands (375 m) under daytime solar illumination. The DNB was designed to map light sources at night. Similar to its predecessor, the Defense Meteorological Satellite Program Operational Linescan System (OLS), the VIIRS DNB should be suitable to identifying bioluminescence at night. However, with its finer resolution and higher SNR than OLS, the VIIRS DNB is demonstrated here to be also able to complement other sensors in the detection and mapping of oil spills

Identifying Industrial Heat Sources Using Time-series of the VIIRS Nightfire Product with an Object-oriented Approach

Carbon-based fuels burned at industrial facilities account for a large proportion of greenhouse gas emissions, and an up-to-date spatiotemporally detailed inventory is essential for a better understanding of global carbon emission patterns. The Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product offers a quantitative estimation of the temperatures of sub-pixel heat sources, providing the potential for detecting thermal anomalies from industrial sectors across the globe. However, identifying subcategories of various industrial heat sources is challenging because there are scarcely any stable and typical characteristics for their classification at a single thermal anomaly scale. Specifically, these nighttime thermal anomalies exhibit a strong spatiotemporal heterogeneity (e.g., fluctuations in retrieved temperature, spatial shifts in position, and presence of false positives), even in industrial heat sources that do not vary through time. Here, we demonstrate an object-oriented approach to robustly segment and accurately classify various industrial heat sources from a time-series of the VIIRS Nightfire product. The approach operates from the cluster level of spatially adjacent nighttime thermal anomalies (i.e., nighttime-heat-source objects rather than individual thermal anomalies) to generate fingerprint-like characteristics and to address the challenge of spatiotemporal heterogeneity. Specifically, the spatial-aggregation characteristic of nighttime thermal anomalies from continuously operating industrial heat sources and the temporal-aggregation characteristics of biomass burnings were incorporated to differentiate industrial nighttime-heat-source objects from ubiquitous biomass burnings. Subsequently, the similarity of the thermal signals of nighttime thermal anomalies from identical industrial heat sources was used to generate highly recognizable characteristics for their identification. A spatiotemporally detailed inventory of industrial heat sources across the globe was then established from this object-oriented classification. The inventory included a total of 15,199 industrial heat sources, representing 49.52% of all higher confidence nighttime thermal anomalies in the VIIRS Nightfire product. Validation of the results showed that only 218 objects (1.43%) were biomass burnings or active volcanoes that were misclassified as industrial heat sources. Further validation of sub-categories indicated an overall classification accuracy of ~ 77%. Our findings suggest that the VIIRS Nightfire product has great potential for monitoring the global distribution and dynamics of industrial heat sources, and combined with the object-oriented approach developed here the methodology is simple, robust, and cost-effective

MODIS Observations of the Bottom Topography and Its Inter-annual Variability of Poyang Lake

Using MODIS 250-m resolution data, we developed a novel approach to derive the bottom topography of Poyang Lake, the largest freshwater lake of China (\u3e 3000 km2 at maximum inundation) for every year between 2000 and 2009. The approach differs from other traditional methods (sonar, Lidar, optical inversion, and Radar) but takes advantage of the fast-changing nature of the lake\u27s inundation area. On every image, the water/land boundary is effectively a topographic isobath after correction for the water level gradient. Thus, the ~ 10/year carefully selected MODIS images provided incremental topographic isobaths, from which bottom topography was derived every year. Such-derived topographic maps were validated using limited historical data and other consistency checks. Most of the lake bottom showed an elevation of 12 m to 17 m (referenced against the elevation reference of the Woosung Horizontal Zero). Significant inter-annual variability of the bottom topography from 2000 to 2009 was found for some of the lake\u27s bottom, with more areas associated with bottom elevation increases than decreases. The changes and inter-annual variability in the bottom topography were attributed to the combined effect of human activities (e.g., sand dredging and levee construction) and weather events. One example was the increased bottom elevation from 2002 to 2003, which was apparently due to the excessive precipitation in 2002 and the impoundment of the Three-Gorges Dam in 2003. The 10-year record of the bottom topography of this highly dynamic lake provides baseline information to monitor the impact of future engineering and management activities, to estimate the lake\u27s water and sediment budgets, and to aid ship navigation

Recovering Low Quality MODIS-Terra Data Over Highly Turbid Waters through Noise Reduction and Regional Vicarious Calibration Adjustment: A Case Study in Taihu Lake

Remote sensing of water quality in turbid coastal and inland waters requires accurate atmospheric correction, which is technically challenging. While previous efforts have shown the advantage of using the short-wave infrared (SWIR) bands instead of near-infrared (NIR) bands for atmospheric correction, such an approach could only be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite (MODISA). This is because MODIS data from the Terra satellite (MODIST) contain more noise and other sensor artifacts, thus this sensor has been generally regarded by the ocean color research community as not being able to provide science quality data. Here, we address this technical challenge through noise reduction and regional vicarious calibration adjustment, and demonstrate preliminary success using turbid Taihu Lake as an example. The noise in the three SWIR bands was evaluated first, and then reduced through a noise reduction method. The SWIR bands were adjusted over open-ocean waters using the well-calibrated NIR ocean bands (1-km resolution) and radiative transfer, which were then used to adjust the land bands (250-m and 500-m resolution) in the visible and NIR over turbid waters where concurrent field-measured reflectance spectra are available. Of all three combinations of SWIR bands, the combination of 1240 and 1640-nm bands was found to perform the best, showing significantly improved retrieval accuracy for Taihu Lake, leading to recovery of low-quality MODIST data to higher-quality data comparable to MODISA, and thus doubling valid data coverage. Testing of this approach on another highly turbid lake (Chaohu Lake, China) showed similar results. While the general application of this approach to turbid lakes still needs to be tested as local tuning of the calibration coefficients may be required, these results suggest that MODIST may be used as effectively as MODISA for monitoring Taihu Lake water quality

Developing a Smart Semantic Web with Linked Data and Models for Near-Real-Time Monitoring of Red Tides in the Eastern Gulf of Mexico

In recent decades, the technology used to detect and quantify harmful algal blooms (commonly known as red tides) and characterize their physicochemical environment has improved considerably. A remaining challenge is effective delivery of the information generated from these advances in a user-friendly way to a diverse group of stakeholders. Based on existing infrastructure, we establish a Web-based system for near-real-time tracking of red tides caused by the toxic dinoflagellate Karenia brevis, which annually threatens human and environmental health in the eastern Gulf of Mexico. The system integrates different data products through a custom-made Web interface. Specifically, three types of data products are fused: 1) near-real-time ocean color imagery tailored for red tide monitoring; 2) K. brevis cell abundance determined by sample analysis; and 3) ocean currents from a nested and validated numerical model. These products are integrated and made available to users in Keyhole Markup Language (KML) format, which can be navigated, interpreted, and overlaid with other products in Google Earth. This integration provides users with the current status of red tide occurrence (e.g., location, severity, and spatial extent) while presenting a simple way to estimate bloom trajectory, thus delivering an effective method for near-real-time tracking of red tides