Analysis and simulation of watershed-scale processes requires spatial characterization of land use, including discrimination among crop types. If this crop type information could be obtained accurately from remote sensing data, the effort required would be significantly reduced, especially for large watersheds. The objective of this study was to use multiple satellite remote sensing datasets to delineate land cover, including crop type, for the Salt River/Mark Twain Lake basin in northeast Missouri. Landsat visible and near-infrared satellite images obtained at multiple dates in the growing season were used to create an unsupervised classification map of land cover. Normalized difference vegetation index (NDVI) maps obtained on a 16-day cycle from MODIS satellite images were used as ancillary data to derive seasonal NDVI trends for each class in the classification map. Tree analysis was applied to the NDVI trend data to group similar classes into clusters, and land cover for each cluster was determined from ground truth data. Additional ground truth data was used to assess the accuracy of the procedure, and crop acreage estimates were compared to county-level statistics. The results of this study showed that land cover and crop type can be delineated by variations in seasonal NDVI trends obtained from remotely sensed data. Maps developed using this process will be useful as input data to environmental analysis models.