Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007-2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.