Discriminant analysis classification (DAC) and decision tree classifiers (DTC) were used for digital mapping of soil drainage in the Bras-d’Henri watershed (QC, Canada) using earth observation data (RADARSAT-1 and ASTER) and soil survey dataset. Firstly, a forward stepwise selection was applied to each land use type identified by ASTER image in order to derive an optimal subset of soil drainage class predictors. The classification models were then applied to these subsets for each land use and merged to obtain a digital soil drainage map for the whole watershed. The DTC method provided better classification accuracies (29 to 92%) than the DAC method (33 to 79%) according to the land use type. A similarity measure ( ) was used to compare the best digital soil drainage map (DTC) to the conventional soil drainage map. Medium to high similarities ( ) were observed for 83% (187？km2) of the study area while 3% of the study area showed very good agreement ( ). Few soil polygons showed very weak similarities ( ). This study demonstrates the efficiency of combining radar and optical remote sensing data with a representative soil dataset for producing digital maps of soil drainage. 1. Introduction Much of Canada’s agricultural land has been mapped at reconnaissance (scale 1？:？125？000) or semidetailed scales (scale 1？:？50 000 or 1？:？63 000). Although these maps are useful for watershed management purposes, the soil information contained within them is often obsolete is typically of poor uniformity, and, nearly, always lacks the precision required to support land use decision-making or modelling efforts. The production of maps at more detailed scales, as well as the inclusion of additional soil drainage information, is thus required for map users to effectively inform land use and management decisions [1]. Mapping soil drainage classes with accurate and objective information is important for both crop productivity and hydrological modelling. Conventional soil mapping methods are laborious and expensive, requiring intensive soil sampling over large areas. Soil surveyors usually identify typological landscape units by their characteristics (i.e., form, geomorphology, vegetation, geology, etc.) and then select representative locations for describing and sampling soil profiles. The number of profiles needed to represent a landscape unit depends on the prospective scale and soil variability. In addition, conventional soil maps are generally created using a polygon-based approach, where different soils on the landscape are represented as polygons with discrete borders.
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