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Koschmieder proposed that visibility is inversely proportional to the extinction coefficient of air, and this model has been widely adopted during the past century. Using radiative transfer theory, the authors present a general relationship for the law of contrast reduction and point out that the Koschmieder model is workable only to situations when a common-size object can be viewed tens of kilometers away. However, the Koschmieder model is not applicable for viewable distances of hundreds of meters when the angular dimension of an object is significantly greater than the eye resolution of the human being. The authors further separate the term 'visible' into 'simple detection' or 'detectability' and 'clear identification' or 'identifiability' and point out that the Koschmieder model is applicable to identifiability, but not necessarily for detectability. In addition, the way of calculating contrast is revised to follow the concept of brightness constancy. The results of this effort advocate the measurement and distribution of detectability in harsh weather conditions, as such data offer more useful and important information for daily life. [ABSTRACT FROM AUTHOR]

The Airborne Remote Optical Spotlight System (AROSS) family of sensors consists of airborne imaging systems that provide passive, high-dynamic range, time series image data and has been used successfully to characterize currents and bathymetry of nearshore ocean, tidal flat, and riverine environments. AROSS-multispectral polarimeter (AROSS-MSP) is a 12-camera system that extends this time series capability to simultaneous color and polarization measurements for the full linear polarization of the imaged scene in red, green, and blue, and near-infrared (RGB-NIR) wavelength bands. Color and polarimetry provide unique information for retrieving dynamic environmental parameters over a larger area (square kilometers) than is possible with typical in situ measurements. This particular field of optical remote sensing is developing rapidly, and simultaneous color and polarimetric data are expected to enable the development of a number of additional important environmental data products, such as the improved ability to image the subsurface water column or maximizing wave contrast to improve oceanographic parameter retrievals of wave spectra and wave heights. One of the main obstacles to providing good-quality polarimetric image data from a multicamera system is the ability to accurately merge imagery from the cameras to a subpixel level. This study shows that the imagery from AROSS-MSP can be merged to an accuracy better than one-twentieth of a pixel, comparing two different automated algorithmic techniques. This paper describes the architecture of AROSS-MSP, the approach for providing simultaneous color and polarization imagery in space and time, an error analysis to characterize the measurements, and example color and polarization data products from ocean wave imagery. [ABSTRACT FROM AUTHOR]