Early wheat yield estimation at field-level by photosynthetic pigment unmixing using Landsat 8 image series

This paper presents a novel approach for estimating wheat yields based on estimated abundances of endmembers attributed to photosynthetic pigments, using Landsat 8 images acquired during maximum greenness. Endmembers within the wheat pixels are found using an unmixing algorithm then they are further optimized to maximize the predictive power of the abundances for the yields. Similarity of the endmembers to photosynthetic pigment's spectral signatures and their predictive power suggests their relevance to the pigments. Although the initial unmixing of the intimate mixture of photosynthetic pigments is linear, interactions of abundances are used in the optimization to handle the non-linearity using bilinear model. Wheat yields are estimated with abundances, their relevant interactions, agrometeorological parameters and vegetation indices using three machine learning algorithms. Harvester records from 142 fields are used as ground truth for performance assessment. The yields are estimated with 82% accuracy (RMSE = 22.51) when Random Forest algorithm is used with important parameters.