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Exploiting spectral variation from crop phenology for agricultural land-use classification

The mapping of agricultural-land use systems using single-date information has, in the past, met with limited success. Broad-band multi-spectral sensors have been used primarily for mapping of broad land cover types, but have been less successful for identifying species-level variation. Hyperspectral sensors have had some success for species mapping, but these images often cover a small area and are not appropriate for large-scale land-use assessment. Phenological changes in crop broad-band spectral properties over the growing season offer a promising method of detecting species variation associated with growth rates, plant structure and cropping practices. This paper will present preliminary results of the use of multi-temporal optical imagery for mapping agricultural species. Three SPOT-4 multispectral scenes were acquired during early, middle and late season growth stages over an agricultural region in eastern Ontario, Canada in 2004. Three supervised classification methods were compared: Maximum-Liklihood, Decision Tree and Neural Network approaches. The impact of atmospheric correction was explored to determine if statistical models using multi-sensor, multi-date inputs are sensitive to differences in atmospheric conditions during image acquisition. The success of each method is assessed based on classification accuracies determined using an independent set of ground measurements. Preliminary results indicate that multi-date information is essential to deriving accurate land use information, and that further inputs in addition to remote sensing data may be needed to define specific classes.