The Potential and Limitations of a Clustering Approach for the Improved Efficiency of Multiple Endmember Spectral Mixture Analysis in Plant Production System Monitoring

Due to the subpixel contribution of background soils and shadows, hyperspectral image interpretation in agricultural management is often constrained. In this paper, the potential of multiple endmember spectral mixture analysis (MESMA) to simultaneously extract the subpixel cover fraction and pure spectral signature of the crop component from a mixed hyperspectral signal is evaluated. Radiative transfer models are used to build lookup tables (LUTs) for both the crop and the soil component, but the extensiveness of the LUTs will decrease the efficiency and operational implementation of MESMA. A clustering procedure is therefore presented, allowing a more efficient use of the LUTs in the MESMA model. The performance of MESMA, using clustered and nonclustered LUTs, to extract the cover fraction and the spectral signature of plant canopies was evaluated using 200 simulated mixtures generated from in situ measured hyperspectral data of soil and citrus canopies. Clustering of the LUT resulted in a more efficient and accurate estimation of the pure subpixel vegetation signal (rmse = 0.097 stabilizing at 40 iterations) compared to a nonclustered LUT (rmse = 0.11 stabilizing at 200 iterations). The subpixel cover fraction estimations, on the other hand, stabilize for both methods around 100 iterations, with an rmse of 0.15 for both approaches. The clustering of the LUT will thus increase both the efficiency and the accuracy of MESMA for estimating the spectral signature of crops while, on average, maintaining the accuracy for the cover fraction estimates. This will enable a more accurate extraction of plant production parameters, which opens up new opportunities regarding precision farming.