For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.

/pdf/deriving-inherent-optical-properties-from-water-color-a-4gmos8ca0n.pdf

Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters

Measurements of the dependence of snow albedo on wavelength, zenith angle, grain size, impurity content, and cloud cover can be interpreted in terms of single-scattering and multiple-scattering radiative transfer theory. Ice is very weakly absorptive in the visible (minimum absorption at λ = 0.46 µm) but has strong absorption bands in the near infrared (near IR). Snow albedo is therefore much lower in the near IR. The near-IR solar irradiance thus plays an important role in snowmelt and in the energy balance at a snow surface. The near-IR albedo is very sensitive to snow grain size and moderately sensitive to solar zenith angle. The visible albedo (for pure snow) is not sensitive to these parameters but is instead affected by snowpack thickness and parts-per-million amounts (or less) of impurities. Grain size normally increases as the snow ages, causing a reduction in albedo. If the grain size increases as a function of depth, the albedo may suffer more reduction in the visible or in the near IR, depending on the rate of grain size increase. The presence of liquid water has little effect per se on snow optical properties in the solar spectrum, in contrast to its enormous effect on microwave emissivity. Snow albedo is increased at all wavelengths as the solar zenith angle increases but is most sensitive around λ =1 µm. Many apparently conflicting measurements of the zenith angle dependence of albedo are difficult to interpret because of modeling error, instrument error, and inadequate documentation of grain size, surface roughness, and incident radiation spectrum. Cloud cover affects snow albedo both by converting direct radiation into diffuse radiation and also by altering the spectral distribution of the radiation. Cloud cover normally causes an increase in spectrally integrated snow albedo. Some measurements of spectral flux extinction in snow are difficult to reconcile with the spectral albedo measurements. The bidirectional reflectance distribution function which apportions the reflected solar radiation among the various reflection angles must be known in order to interpret individual satellite measurements. It has been measured at the snow surface and at the top of the atmosphere, but its dependence on wavelength, snow grain size, and surface roughness is still unknown. Thermal infrared emissivity of snow is close to 100% but is a few percent lower at large viewing angles than for overhead viewing. It is very insensitive to grain size, impurities, snow depth, liquid water content, or density. Solar reflectance and microwave emissivity are both sensitive to various of these snowpack parameters. However, none of these parameters can be uniquely determined by satellite measurements at a single wavelength; a multichannel method is thus necessary if they are to be determined by remote sensing.

Optical Properties of Snow

The remote-sensing reflectance Rrs is not
directly measurable, and various methodologies have been employed in
its estimation. I review the radiative transfer foundations of
several commonly used methods for estimating
Rrs, and errors associated with estimating
Rrs by removal of surface-reflected sky radiance
are evaluated using the Hydrolight radiative transfer numerical
model. The dependence of the sea surface reflectance factor ρ,
which is not an inherent optical property of the surface, on sky
conditions, wind speed, solar zenith angle, and viewing geometry is
examined. If ρ is not estimated accurately, significant errors
can occur in the estimated Rrs for near-zenith
Sun positions and for high wind speeds, both of which can give
considerable Sun glitter effects. The numerical simulations suggest
that a viewing direction of 40 deg from the nadir and 135 deg from the
Sun is a reasonable compromise among conflicting requirements. For
this viewing direction, a value of ρ ≈ 0.028 is acceptable
only for wind speeds less than 5 m s-1. For
higher wind speeds, curves are presented for the determination of ρ
as a function of solar zenith angle and wind speed. If the sky is
overcast, a value of ρ ≈ 0.028 is used at all wind
speeds.

Estimation of the remote-sensing reflectance from above-surface measurements.

The assumption that values of water-leaving radiance in the near-infrared (NIR) are negligible enable aerosol radiative properties to be easily determined in the correction of satellite ocean color imagery. This is referred to as the black pixel assumption. We examine the implications of the black pixel assumption using a simple bio-optical model for the NIR water-leaving reflectance [rho(w)(lambda(NIR))](N). In productive waters [chlorophyll (Chl) concentration >2 mg m(-3)], estimates of [rho(w)(lambda(NIR))](N) are several orders of magnitude larger than those expected for pure seawater. These large values of [rho(w)(lambda(NIR))](N) result in an overcorrection of atmospheric effects for retrievals of water-leaving reflectance that are most pronounced in the violet and blue spectral region. The overcorrection increases dramatically with Chl, reducing the true water-leaving radiance by roughly 75% when Chl is equal to 5 mg m(-3). Relaxing the black pixel assumption in the correction of Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) satellite ocean color imagery provides significant improvements in Chl and water-leaving reflectance retrievals when Chl values are greater than 2 mg m(-3). Improvements in the present modeling of [rho(w)(lambda(NIR))](N) are considered, particularly for turbid coastal waters. However, this research shows that the effects of nonzero NIR reflectance must be included in the correction of satellite ocean color imagery.

/pdf/atmospheric-correction-of-satellite-ocean-color-imagery-the-3sxn3q3svm.pdf

Atmospheric correction of satellite ocean color imagery: the black pixel assumption

Eastern North America receives elevated atmospheric mercury deposition from a combination of local, regional, and global sources. Anthropogenic emissions originate largely from electric utilities, incinerators, and industrial processes. The mercury species in these emissions have variable atmospheric residence times, which influence their atmospheric transport and deposition patterns. Forested regions with a prevalence of wetlands and of unproductive surface waters promote high concentrations of mercury in freshwater biota and thus are particularly sensitive to mercury deposition. Through fish consumption, humans and wildlife are exposed to methylmercury, which markedly bioaccumulates up the freshwater food chain. Average mercury concentrations in yellow perch fillets exceed the Environmental Protection Agency's human health criterion across the region, and mercury concentrations are high enough in piscivorous wildlife to cause adverse behavioral, physiological, and reproductive effects. Initiati...

/pdf/mercury-contamination-in-forest-and-freshwater-ecosystems-in-5ag0tn65sn.pdf

Mercury Contamination in Forest and Freshwater Ecosystems in the Northeastern United States

Dispersion of particulates from an ORTRAN coal transshipment facility was investigated to estimate the input of coal dust into the Duluth harbor and to determine the transport of coal particulates to Lake Superior. A numerical model was used to discuss dispersal of contaminants and determine the residence time of pollutants in the water way. The model was verified using measurements of water levels, currents and water quality parameters.

Sources and transports of coal in the Duluth-Superior Harbor. Research report

Light scattering by individual particulates is used in a multiple-detector system to categorize the composition of suspended solids in terms of broad particulate categories. The scattering signatures of red clay and taconite tailings, the two primary particulate contaminants in western Lake Superior, along with two types of asbestiform fibers, amphibole and chrysotile, were studied in detail. A method was developed to predict the concentration of asbestiform fibers in filtration plant samples for which electron microscope analysis was done concurrently. Fiber levels as low as 5 x 10(4) fibers/liter were optically detectable. The method has application in optical categorization of samples for remote sensing purposes and offers a fast, inexpensive means for analyzing water samples from filtration plants for specific particulate contaminants.

Michael Sydor

Papers

Sources and transports of coal in the Duluth-Superior Harbor. Research report

Analysis of suspended solids by single-particle scattering. [for Lake Superior pollution monitoring]

Erratum: ‘‘Photoreflectance measurements of unintentional impurity concentrations in undoped GaAs’’ [J. Appl. Phys. 66, 156 (1989)]