title: Probability theory for 3-layer remote sensing radiative transfer model: univariate case.
abstract: A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics.
authors.affiliation: RDECOM, Edgewood Chemical Biological Center, Aberdeen Proving Ground, Maryland 21010, USA. avishai.bendavid@us.army.mil
authors.forename: Avishai
authors.initials: A
authors.lastname: Ben-David
authors.forename: Charles E
authors.initials: CE
authors.lastname: Davidson
mesh_terms: D001272:Atmosphere / Q000032:analysis* / Q000737:chemistry*; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D008027:Light; D015233:Models, Statistical; D009391:Nephelometry and Turbidimetry / Q000379:methods*; D058998:Remote Sensing Technology / Q000379:methods*; D012542:Scattering, Radiation