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Two reciprocal direct and inverse radiative transfer models dealing with hyperspectral remote sensing in the visible-to-shortwave-infrared spectral domain are described in this paper. The first one, called COMANCHE, considers a flat and heterogeneous ground scene, with bidirectional reflectance effects, and computes spectral radiance hypercubes at the sensor level. Trapping and environment phenomena are take into account through specific optimized Monte Carlo modules. The reciprocal inverse algorithm, called COCHISE, considers a sensor-level hyperspectral image and retrieves the ground spectral reflectance distribution as well as the water vapor content. COCHISE removes the atmospheric and environment effects with the same modeling as COMANCHE, but consider however the Lambertian assumption for the ground reflectance. Both of the models are validated with existing radiative transfer codes (MODTRAN and AMARTIS, for instance), and also using experimental datasets from the Airborne Visible/Infrared Imaging Spectrometer. The comparisons show very good agreement regarding to the usual uncertainties involved insuche experiment. COCHISE is also applied on a additional dataset acquired by HyMap.