Spectral remote sensing has evolved considerably from the early days of airborne scanners of the 1960's and the first Landsat multispectral satellite sensors of the 1970's. Today, airborne and satellite hyperspectral sensors provide images in hundreds of contiguous narrow spectral channels at spatial resolutions down to meter scale and spanning the optical spectral range of 0.4 to 14 m. Spectral reflectance and emissivity databases find use not only in interpreting these images but also during simulation and modeling efforts. However, nearly all existing databases have measurements of materials under pristine conditions. The work presented extends these measurements to nonpristine conditions, including materials contaminated with sand and rain water. In particular, high resolution spectral reflectance and emissivity curves are presented for several man-made surfaces ͑asphalt, concrete, roofing shingles, and vehicles͒ under varying amounts of sand and water. The relationship between reflectance and area coverage of the contaminant is reported and found to be linear or nonlinear, depending on the materials and spectral region. In addition, new measurement techniques are presented that overcome limitations of existing instrumentation and laboratory settings. These measurements enable simulation of optical images with objects in the scene attributed with realistic surface reflectance and emissivity spectra.

Abstract-The primary interest of this research is to introduce selected environmental effects into RIT's Digital Imaging andRemote Sensing Image Generation (DIRSIG) Model. DIRSIG is capable of producing high resolution images (meter scale) using Computer Aided Design models (CAD) of buildings, vehicles, trees, etc. across the full optical spectrum (0.35-25µm). Currently, these objects are modeled in a pristine manner and there is no option to simulate them after exposure to environmental effects.Ideally, we would like to subject a given material to these environmental effects and then accurately model the modified reflected or emitted spectrum. As a first step, we have chosen to model moisture and dust on surfaces by implementing a model of the effects of a thin layer of water and soil coverage, respectively, on the spectral reflectance and emittance of different materials.Using new techniques for field instruments in a laboratory setting, we have established the relationship between the surface contaminant and its effect on the target in question. These results will be incorporated into the DIRSIG modeling tool for wider use.