This paper describes an efficient method for retrieval of ground reflectance characteristics of targets from calibrated multispectral airborne video data for routine operational airborne missions. The method uses a simplified atmospheric scattering model in combination with a dark-object subtraction procedure to estimate the effect of the atmosphere in the path between the target and the sensor, as well as the adjacent environmental effect, on the radiation signal received by an airborne sensor. The simplicity of the atmospheric scattering model is maintained by the assumption that the air density within the targetsensor path in the lower atmosphere is sufficiently uniform for operations of the Charles Sturt University's (CSU) Multispectral Airborne Video System (MAVS). The MAVS acquires imagery in blue, green, red and near-infrared (NIR) narrow spectral bands. The MAVS is radiometrically calibrated and has a consistent radiometric response in-flight. An important feature of the new method is the coupling of the image based brightness data (DN) of a dark-object and the system radiometric calibration coefficients to determine the path reflectance and the environmental reflectance of the target. The sum of the path reflectance and the environment reflectance is known as haze reflectance. The haze reflectance indicates the amount of atmospheric haze in the airborne imagery. The simplified atmospheric model is then employed to determine the actual ground reflectance of the targets using the haze subtracted apparent (total) reflectance of the target at the altitude of the airborne sensor. The apparent reflectance of the target at the sensor altitude is obtained directly from the image based DN data and the system radiometric calibration coefficients. An interesting aspect of this simplified method is that an estimate of the environmental reflectance can be obtained as a by-product of the atmospheric haze calculation using a dark-object subtraction technique. The retrieved ground reflectance characteristics from calibrated MAVS imagery are now being used routinely for remote quantitative monitoring of agricultural and environmental targets.