Modeling optical properties of human skin using Mie theory for particles with different size distributions and refractive indices.

We used size distributions of volume equivalent spherical particles with complex refractive index to model the inherent optical properties (IOPs) in four different layers of human skin at ten different wavelengths in the visible and near-infrared spectral bands. For each layer, we first computed the size-averaged absorption coefficient, scattering coefficient, and asymmetry factor for the collection of particles in a host medium using Mie theory and compared these IOPs in each layer with those obtained from a bio-optical model (BOM). This procedure was repeated, using an optimization scheme, until satisfactory agreement was obtained between the IOPs obtained from the particle size distribution and those given by the BOM. The size distribution as well as the complex refractive index of the particles, obtained from this modeling exercise, can be used to compute the phase matrix, which is an essential input to model polarized light transport in human skin tissue.

Optical remote sensing of waters with vertical structure.

Optical remote sensing of ocean color is a well-established technique that is used to produce maps of marine constituents on a routine basis. Retrieval algorithms used to infer pigment concentrations from measurements of ocean color are usually based on the assumption that the upper ocean column is vertically homogeneous. However, stable stratification of the water column is often encountered in coastal waters and in fjords. This stratification is decisive for the initiation, maintainance, and species composition of phytoplankton blooms. Here we present an optical remote-sensing algorithm with the ability to resolve such a vertical structure of oceanic waters. The vertical structure is assumed to consist of two homogeneous layers with different concentrations of chlorophyll a. The algorithm is designed to determine the chlorophyll-a concentrations of the two layers as well as the thickness of the upper layer. These three parameters influence the ocean color and are simultaneously retrieved through an inverse-modeling technique. This technique consists of using radiative-transfer computations for a coupled atmosphere-ocean system to simulate radiances received in various bands of the satellite sensor and to compare these simulated results with measured radiances. The sum of absolute values of differences between simulated and measured radiances is minimized by use of an optimization algorithm, and the retrieved parameters are those that yield the minimum sum of differences between measured and simulated data. The optimization algorithm that we used in our study is the simulated annealing method, which is an extension of the downhill simplex algorithm. In this study the algorithm was tested on synthetic data generated by the forward model. The results indicate that it should be possible to retrieve vertical variations in the pigment concentration. The synthetic data were generated for spectral bands that coincide with those of the Medium Resolution Imaging Spectrometer sensor, which will be a part of the instrument package of the upcoming Environmental Satellite.

Optical remote sensing of marine constituents in coastal waters: a feasibility study.

Optical remote sensing of ocean color is a well-established technique for inferring ocean properties. However, most retrieval algorithms are based on the assumption that the radiance received by satellite instruments is affected only by the phytoplankton pigment concentration and correlated substances. This assumption works well for open ocean water but becomes questionable for coastal waters. To reduce uncertainties associated with this assumption, we developed a new algorithm for the retrieval of marine constituents in a coastal environment. We assumed that ocean color can be adequately described by a three-component model made up of chlorophyll a, suspended matter, and yellow substance. The simultaneous retrieval of these three marine constituents and of the atmospheric aerosol content was accomplished through an inverse-modeling scheme in which the difference between simulated radiances exiting the atmosphere and radiances measured with a satellite sensor was minimized. Simulated radiances were generated with a comprehensive radiative transfer model that is applicable to the coupled atmosphere-ocean system. The method of simulated annealing was used to minimize the difference between measured and simulated radiances. To evaluate the retrieval algorithm, we used simulated (instead of measured) satellite-received radiances that were generated for specified concentrations of aerosols and marine constituents, and we tested the ability of the algorithm to retrieve assumed concentrations. Our results require experimental validation but show that the retrieval of marine constituents in coastal waters is possible.