Chuanmin Hu, Lian Feng, Jamie Holmes, Gregg A. Swayze, Ira Leifer, Christopher Melton, Oscar Garcia, Ian MacDonald, Mark Hess, Frank Muller-Karger, George Graettinger, Rebecca Green
The Deepwater Horizon (DWH) oil blowout in the Gulf of Mexico (GoM) led to the largest offshore oil spill in U.S. history. The accident resulted in oil slicks that covered between 10,000 and upward of 40,000 km2 of the Gulf between April and July 2010. Quantifying the actual spatial extent of oil over such synoptic scales on an operational basis and, in particular, estimating the oil volume (or slick thickness) of large oil slicks on the ocean surface has proven to be a challenge to researchers and responders alike. This challenge must be addressed to assess and understand impacts on marine and coastal resources and to prepare a response to future spills. We estimated surface oil volume and probability of occurrence of different oil thicknesses during the DWH blowout in the GoM by combining synoptic measurements (2330-km swath) from the satellite-borne NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and near-concurrent, much narrower swath (∼5 km) hyperspectral observations from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). A histogram-matching approach was used to transfer AVIRIS-derived oil volume to MODIS pixel-scale dimensions, after masking clouds under both sun glint and nonglint conditions. Probability functions were used to apply the transformation to 19 MODIS images collected during the DWH event. This generated three types of MODIS oil maps: maps of surface oil volume, maps of relative oil thickness with four different classes (i.e., 0 μm, <0.08 μm, 0.08 to 8 μm, and >8 μm), and maps of probability distributions of different thicknesses. The results were compared with satellite-based synthetic aperture radar measurements and evaluated with concurrent aerial photographs. Although the methods may not be ideal and the results may contain large uncertainties, the current attempt suggests that coarse-resolution optical remote sensing observations can provide estimates of relative oil thickness/volume for large oil slicks captured by satellites.
Satellite ocean color remote sensing techniques, coupled with in situ data, were used to examine the spatial extent and evolution of four Karenia brevis blooms on the West Florida Shelf (WFS) in 2004, 2005, 2006, and 2011. Observations were obtained with the moderate resolution imaging spectroradiometer (MODIS-Aqua). These four blooms were delineated by combining remote-sensing reflectance at 555 nm and normalized fluorescence line height. In 2004 and 2005, the WFS was affected by several hurricanes, including the category 5 storm Hurricane Katrina. These hurricanes led to increased river discharge and vertical mixing which favored bloom intensification and dispersion. No hurricanes passed over the WSF in 2006; however, storms in south Florida may have aided bloom intensification via increased river discharge. In 2011, a bloom appeared off Venice, Florida, where several small creeks discharge. The bloom moved south toward Charlotte Harbor where it intensified and lingered for several months as it received nutrients from riverine discharge and upwelling events. While it is difficult to identify initiation stages of a K. brevis bloom (<∼50,000 cells L−1) using satellite imagery, the techniques used here provide information about bloom evolution (size, duration, and advection) and insight into factors affecting bloom dynamics.
Several satellite-based methods have been used to detect and trace Karenia brevis red tide blooms in the eastern Gulf of Mexico. Some require data statistics and multiple data products while others use a single data product. Of these, the MODIS normalized fluorescence line height (nFLH) has shown its advantage of detecting blooms in waters rich in colored dissolved organic matter, thus having been used routinely to assess bloom conditions by the Florida Fish and Wildlife Conservation Commission (FWC), the official state agency of Florida responsible for red tide monitoring and mitigation. However, elevated sediment concentrations in the water column due to wind storms can also result in high nFLH values, leading to false-positive bloom interpretation. Here, a modified nFLH data product is developed to minimize such impacts through empirical adjustments of the nFLH values using MODIS-derived remote sensing reflectance in the green band at 547 nm. The new product is termed as an algal bloom index (ABI), which has shown improved performance over the original nFLH in both retrospective evaluation statistics and near real-time applications. The ABI product has been made available in near real-time through a Web portal and has been used by the FWC on a routine basis to guide field sampling efforts and prepare for red tide bulletins distributed to many user groups.
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 [K d (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.
KEYWORDS: Signal detection, Signal to noise ratio, Sensors, Interference (communication), Atmospheric sensing, Atmospheric propagation, Atmospheric modeling, Remote sensing, Reflectivity, Water
Over the decades, ocean color imaging sensors placed in Low Earth Orbits (LEO) have enabled nearly daily measurements of ocean water properties. Such observations, however, are restricted by cloud/atmospheric conditions. More importantly, such systems could not provide sufficient number of measurements to study the diurnal dynamics of coastal/oceanic ecosystems. One way to surmount such limitations is to leverage geo-stationary orbits to significantly improve temporal observations over such dynamical coastal/oceanic environments. In this study, it is desired to examine whether 50% changes in chlorophyll-a concentration (< 1.5 ug⁄l) on a semi-diurnal basis are above the noise level. To do so, the top-of-atmosphere radiance (Lt) is modeled for the planned GEO-CAPE mission intended for monitoring coastal ecosystem and river plumes. The input to the simulations includes diurnal remote sensing reflectances (Rrs), which are propagated through a moderately clear atmospheric conditions using a radiative transfer code. The simulations are carried out for two footprints to investigate two extremely different sun-sensor geometries. From these simulations, the temporal change in spectral reflectances between the hours relative to an average noise is examined. Based on the preliminary results, it was found that while the signal change is, on average, 13x the average noise for near-nadir footprints, the change in signal, on average, is only 1.5x the average noise level for near-edge footprints at top of the atmosphere. Such a contrast suggests difficulties in retrieving diurnal variability for locations near the edge of the field of regard (FOR).
Wetlands are important ecosystems on Earth. However, global wetland coverage is being reduced due to both anthropogenic and natural effects. Thus, assessment of temporal changes in vegetative coverage, as a measure of the wetland health, is critical to help implement effective management plans and provide inputs for climate-related research. In this work, 596 moderate-resolution imaging spectroradiometer (MODIS) 250-m resolution images of the Hongze Lake national wetland nature reserve from 2000 to 2009 were used to study the vegetative coverage (above the water surface) of the reserve. Three vegetation indices [normalized difference vegetation index (NDVI), enhanced VI (EVI), and floating algae index (FAI)] were compared to evaluate their effectiveness in assessing relative changes. FAI was less sensitive than NDVI and EVI to aerosol effects and showed less statistical error than NDVI and EVI. Long-term FAI data revealed clear seasonal cycles in vegetative coverage in the 113-km 2 core area of the reserve, with annual maximal coverage relatively stable after 2004. This suggests that the national wetland nature reserve was well protected through the study period. However, vegetative coverage decreased due to the flooding event in 2003. Moreover, correlation analysis showed that annual sunshine duration collectively played a significant role in affecting the wetland vegetative coverage.
Observing eddies and other oceanographic patterns in the subtropical and tropical oceans during summer time can be
problematic, because sea surface temperature often lacks spatial contrast and satellite altimetry provides coarse
resolution data with some time lag. MODIS ocean color observations are supposed to provide timely information, but
they suffer from sun glint contamination when the glint reflectance, Lg, is > 0.01 sr-1. Here, an empirical approach is
demonstrated to remove sun glint and clouds using MODIS Rayleigh-corrected reflectance (Rrc) at 469, 555, 645, 859,
and 1240-nm. A color index (CI) is derived from the 469-555-645 bands using a baseline subtraction. The CI color
patterns appear consistent from adjacent days when different glint and aerosol patterns are present, suggesting the
validity of the approach. Applications of the approach over the Gulf of Mexico and other tropical and subtropical regions
further validate the approach's general applicability. The new products at 1-km and 500-m resolutions make it possible
to observe ocean eddies at both large and small scales. The simple design of the approach also makes it straightforward
to implement for other regions when a qualitative MODIS CI is desired to infer circulation patterns and to detect eddies
under severe sun glint.
The Deepwater Horizon oil spill presented an unprecedented threat to the Gulf of Mexico coastline and living marine
resources, and possibly to that of the southeastern USA. Needed for mitigation efforts and to guide scientific
investigations was a system for tracking the oil, both at the surface and at depth. We report on such system, implemented
immediately upon spill onset, by marshaling numerical model and satellite remote sensing resources available from
existing coastal ocean observing activities. Surface oil locations inferred from satellite imagery were used to initialize the
positions of the virtual particles in an ensemble of trajectory models, and the particles were tracked using forecast
surface currents, with new particles added to simulate the continual release of oil from the well. Three dimensional
subsurface tracking were also performed from the well site location at several different depths. Timely trajectory
forecasts were used to plan scientific surveys and other spill response activities.
Optical properties of oceanic and coastal waters are not only important for describing subsurface light field, but also
useful indexes of environmental status. To meet the demand of various users, optical data products of global waters are
now generated from ocean color satellite sensors (e.g. SeaWiFS, MODIS, MERIS). These products, due to imperfect
sensor technology and retrieval algorithms, inherently contain some degrees of uncertainties. Traditionally, an averaged
difference (or so-called error) for a dataset is usually provided via comparing retrieved values with in situ
measurements. This averaged "error" is good at providing an overall picture between the retrieved and measured
properties, but cannot indicate uncertainties for a specific product or a pixel, because that uncertainties in these products
are not spatially uniform. Here, using optical properties derived from the Quasi-Analytical Algorithm as an example, we
present an approach to quantify pixel-wise uncertainties of remote-sensing derived properties. Further, we quantitatively
evaluated the uncertainties of the derived inherent optical properties (IOPs) and water-clarity products with a simulated
dataset, and found that the relative uncertainty is generally within 10% for total absorption coefficients of oceanic
waters. This presentation shows the theoretical basis to evaluate and understand the impacts of the various components
on the analytically derived optical properties, and that a practical means to quantify the uncertainties of inverted
properties for each reflectance spectrum is now available. This effort lays the groundwork for generating quality maps
of optical properties derived from satellite ocean color images.
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 effect this change. The focus 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's MODIS. Specifically, continental-scale high-resolution (1 km) maps of chlorophyll and of 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 serves data and offers simple analysis tools that anyone can use with the ultimate goal of improving coastal assessments, management and policies.
Measurement of spectral remote sensing reflectance is essential to characterize water color, and to further estimate various water constituents with bio-optical algorithms. The measurement is critical to satellite data calibration/validation as well as to bio-optical algorithm development. Unfortunately, commercial instruments for such measurement are expensive, and they are either not capable of displaying data in real-time or not easy to use in coastal environment where large vessels are not applicable. In this paper we describe a simple instrument for such measurement. The fiber-optic Ocean Optics S2000 spectrometer, originally designed for lab use, was further developed to measure remote sensing reflectance. Compared with concurrent measurements from other expensive commercial instruments over different water bodies, the measured spectral reflectance is nearly identical (mean RMS difference < 2%). Through linearity and sensitivity analysis we found that it is capable of characterizing a variety of water types, even though the sensitivity is not as high as its commercial counterparts. The instrument substantially reduces the cost; it has real-time display and is easy to operate (< 0.5 kg) on small vessels. Further, combined with a liquid waveguide and a light source it is also capable of measuring Gelbstoff absorption with sufficient accuracy.
A spectra-matching optimization algorithm, designed for hyperspectral sensors, has been implemented to process SeaWiFS-derived multi-spectral water-leaving radiance data. The algorithm has been tested over Southwest Florida coastal waters. The total spectral absorption and backscattering coefficients can be well partitioned with the inversion algorithm, resulting in RMS errors generally less than 5% in the modeled spectra. For extremely turbid waters that come from either river runoff or sediment resuspension, the RMS error is in the range of 5-15%. The bio-optical parameters derived in this optically complex environment agree well with those obtained in situ. Further, the ability to separate backscattering (a proxy for turbidity) from the satellite signal makes it possible to trace water movement patterns, as indicated by the total absorption imagery. The derived patterns agree with those from concurrent surface drifters. For waters where CDOM overwhelmingly dominates the optical signal, however, the procedure tends to regard CDOM as the sole source of absorption, implying the need for better atmospheric correction and for adjustment of some model coefficients for this particular region.
The Pearl River system is mainly located in the Guangdong Province in southern China, with the length of 2214 km and total area of 453,690 km2. The Pearl River estuary is the largest estuary in the South China Sea (SCS), with a mean annual discharge of 326 billion m3, of which are about 30 million tons of dissolved matters annually discharged into the estuary. The high concentration of suspended sediments and dissolved matters makes the optical properties of the coastal waters very complex.
The spectral absorption coefficient of yellow substance [Ay(λ)] is one of the inherent optical properties that influence the reflectance (or water-leaving radiance) of the water body. It is essential to measure Ay(λ) and to quantify its contributions to the total absorption of the water body. In this study, the Gelbstoff Optical Analyse Laboratory System (GOALS), with spectral range from 200 to 850 nm and with spectral resolution of 0.37 nm per pixel, was used to measure Ay(λ) in the Pearl River estuary and in the adjacent coastal waters in July 2002. Ay(400) was around 1.5 m-1 near the river mouth (zero salinity). It decreases with increasing salinity following an apparent non-linear mixing line. There is no apparent relationship between Ay(400) and dissolved organic carbon (DOC) concentration, indicating that the estuary is a complex, non-point source environment. This presents a great challenge to remote sensing study in this area.
Solar Fraunhofer lines are used as indicators of the inelastic light scattering in the sea water. Data from both in-shore and off-shore are presented and compared with results of theoretical modeling. Very good agreement is found between the modeled and measured proportion of inelastic to elastically scattered and direct light at 589 nm when the Raman scattering coefficient of Marshall and Smith is used, as opposed to that of Slusher and Derr. At 656 nm the agreement is not as good, indicating possible interference from other sources such a Chlorophyll fluorescence. Recent work has extended the measurements of include smaller absorption lines, such as 689 nm, where significant filling has been measured at the surface due to the Chlorophyll fluorescence. This technique allows the natural fluorescence to be measured, even at the surface where there is still a significant amount of direct solar light.
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