Many remote sensing applications rely on accurate spectral estimates of surface reflectance. To transform from measured irradiance to reflectance the contaminating effects of intervening atmosphere must be removed. A model-based algorithm is used to perform this transformation. It operates on hyperspectral data collected in the reflective wavelength region (0.4 to 2.5 mu m) where the state of the atmosphere is described by a set of parameters (e.g. water vapor and aerosol content). The algorithm embeds an atmospheric model (or a derived database) into an estimation loop that sequentially solves for each parameter using measurements in predetermined bands. Estimates of the atmospheric state are then used along with the atmospheric model to develop a set of correction terms, which are applied on a pixel-by-pixel basis across the entire spectrum to convert measured irradiance to reflectance. Emphasis is placed on automation requiring a unique approach to water vapor and visibility estimation.Algorithm performance is demonstrated against AVIRIS and HYDICE collections taken over the Atmospheric Radiation Measurement site near Lament, OK. Estimates of total integrated water vapor and visibility (aerosol content) are compared to external measurements provided by meteorological instruments. Also reflectance estimates of the gray and spectral reflectance panels are compared to field measurements.

A framework is proposed for analyzing ancillary data and developing procedures for incorporating ancillary data to aid interactive identification of land-use categories in land-use updates. The procedures were developed for use within an integrated image processing/geographic information systems (GIS) that permits simultaneous display of digital image data with the vector land-use data to be updated. With such systems and procedures, automated techniques are integrated with visual-based manual interpretation to exploit the capabilities of both. The procedural framework developed was applied as part of a case study to update a portion of the land-use layer in a regional scale GIS. About 75 percent of the area in the study site that experienced a change in land use was correctly labeled into 19 categories using the combination of automated and visual interpretation procedures developed in the study.