Linear unmixing of multidate hyperspectral imagery for crop yield estimation
In this paper, we have evaluated unsupervised unmixing approach (VCA) for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by both approaches are strongly correlated to the yield data (the correlation coefficients are between 0.7 to 0.8). The results validate the higher efficiency of the unsupervised unmixing approaches compared with the supervised methods presented [13] for the purpose of yield estimation. In addition, the unmixing was performed on the hyperspectral images of the same fields, but taken on two different dates. The results show that the correlations between the vegetation abundances and the yield image might change in terms of the observation dates. However, by combining the vegetation abundances extracted on different dates (by computing their products or the square roots of the products), the correlations are significantly improved so that we can always obtain the highest correlations.