Mapping within-field variability in wheat yield and biomass using remote sensing vegetation indices

This paper explored the ability of remote sensing (RS) and meteorological data to map the variability of yield/biomass in cultivated wheat ( Triticum aestivum ). The methodology integrated a time series of RS-based vegetation indices (VI) into a simple model based on the water productivity. Thus, the study analyzed if the biophysical parameters deduced from the VI could be used for the quantification of the differences in crop growth and yield assuming that in operative scenarios, the spatial distribution of the factors limiting the crop growth is unknown. The results of the model were analyzed in terms of the absolute values and the within-field variability with respect to space-continuous measurements of yield and biomass data. The variability registered in the fields was quantified as the ratio between actual yield or biomass in any given location and the mean value for the analyzed variable in each field. The good correlation between measured and modelled variability demonstrated the potential of the proposed approach to reproduce variability even under stress conditions. The proposed approach defined differences in crop growth similar to the ground measurements. The additional evidence obtained point to the necessity of considering the individual time-trajectory of each pixel for the assessment of within field variability. This approach requires the identification of the beginning and the end of the growing cycle. The proposed methodology, based on thresholds of VI offered promising results.