Use of spectral and temporal unmixing for crop identification using multi-spectral data

The reflectance values of pixels, recorded by remote sensors, often result from spectral mixture of a number of ground spectral classes, constituting the area of a pixel. This, the so-called mixed pixel problem, has always been an obstacle in image classification in deriving accurate land cover classes. This study suggests the use of a sub-pixel classification technique: spectral linear unmixing, for an improved crop classification. If mixing is considered linear, then the resulting pixel reflectance is a linear summation of the individual material reflectance multiplied by the surface fraction they constitute. In addition to the problem of mixed pixels, limited spectral separability among different agricultural crop types is another problem that causes inaccuracy in classification. For this reason, linear unmixing is applied to multitemporal Landsat images to take an advantage of the spectral discrepancies shown by crops over the course of their growing cycle. It is expected that the multitemporal profile for each crop’s fraction values will be distinct from each other due to their respective growth cycle and hence an additional aid to improve for the unmixing classification results. These experiments are applied to the municipality of Maasbree in the south of The Netherlands. Three Landsat images, dating May 14, August 01 and August 26, are used for this study. Experiments have shown unique information over time in the spectral-reflectance profiles and vegetation indices of the agricultural crops. Root Mean Square (RMS) images of May14 and Aug01 has shown better accuracy in the unmixing results than for Aug26.