Most of the atmospheric correction methods proposed in the literature are not easily applicable in reaJ cases. The most sophisticated models frequently require inputs which are not commonly available, whilst traditional simple dark object subtraction techniques do not generally give real reflectance values. In the present work an atmospheric correction method applicable to Landsat-TM data is described, which requires only inputs that are commonly available and the presence in the imaged scenes of some dark surfaces in TM bands 1 (blue) and 3 (red). The method consists of an inversion algorithm based on a simplified radiative transfer model in which the characteristics of atmospheric aerosols are estimated by the use of the path radiance in two TM bands rather than a priori assumed. On the basis of this information, which is crucial for determining the atmospheric properties, the retrieval of real reflectances from TM images is possible. The method can be applied to all TM scenes in which some dark points can be realistically supposed to be present, which is particularly advantageous in retrospective studies. Several TM scenes taken from different landscapes and in different seasons were corrected using the model. The reflectance values found were tested against ground measurements and compared with data from the literature. The results show a substantial improvement in the accuracy of the reflectance estimates with respect to estimates without atmospheric correction. Given some care in the identification of dark values, the relative error in actual reflectance retrieval is always rather low (≅10–20 per cent); this error can be considered acceptable for most practical applications.

Spectral mixture modelling has developed in recent years as a suitable remote sensing tool for analysing the biophysical and compositional character of ground surfaces. In this paper the potentiality of the linear spectral mixture model to extract vegetation related parameters from 0·4-2·5 μm reflectance data has been tested. High spectral resolution reflectance measurements of soil-plant mixtures with different soil colour and plant densities were carried out in a laboratory experiment. The constrained least-squares and the factor analysis unmixing procedures were applied to generate endmember fractions of the components present in the mixtures and to test the validity of the model. It is concluded that the derived fraction of the vegetation endmember is less sensitive to soil background than the NDV[. The accuracy attainable by this modelling approach can be considered sufficient for many practical purposes, being operational in the monitoring of vegetation from satellite data.