Automatic mapping of soil texture through the integration of ground, satellite and ancillary data

Satellite images obtained in the optical domain can provide information on important soil properties, such as texture. The use of these images to automatically map soil texture is, however, complicated by the presence of vegetation cover, which can mask the soil spectral response. A multistep methodology based on the use of ground, satellite and ancillary data is proposed and tested to map soil texture in Grosseto, a province of Central Italy. The methodology first separated vegetated and nonvegetated pixels of Landsat Thematic Mapper (TM) images by the use of an appropriate spectral index, the Soil Adjusted Vegetation Index (SAVI). Next, different transforms (nonparametric and parametric) were tuned using ground samples and applied to the two pixel types to separately extract relevant spectral information. The outcomes of these transforms were then merged and subjected to further processing aimed at reducing noise and conveying spatial information to the mapping process. The stratification of the soil texture estimates obtained on different lithological units was finally tested to further improve map accuracy.