In livestock grazing environments, the knowledge of C3/C4 species composition of a pasture field is invaluable, since such information assists graziers in making decisions around fertilizer application and stocking rates. The general aim of this research was to explore the potential of multi-temporal Sentinel-1 (S1) Synthetic Aperture Radar (SAR) to discriminate between C3, C4, and mixed-C3/C4 compositions. In this study, three Random Forest (RF) classification models were created using features derived from polarimetric SAR (polSAR) and grey-level co-occurrence textural metrics (glcmTEX). The first RF model involved only polSAR features and produced a prediction accuracy of 68% with a Kappa coefficient of 0.49. The second RF model used glcmTEX features and produced prediction accuracies of 76%, 62%, and 75% for C3, C4, and mixed C3/C4 grasses, respectively. The glcmTEX model achieved an overall prediction accuracy of 73% with a Kappa coefficient of 0.57. The polSAR and glcmTEX features were then combined (COMB model) to improve upon their individual classification performances. The COMB model produced prediction accuracies of 89%, 81%, and 84% for C3, C4, and mixed C3/C4 pasture grasses, and an overall prediction accuracy of 86% with a Kappa coefficient of 0.77. The contribution of the various model features could be attributed to the changes in dominant species between sampling sites through time, not only because of climatic variability but also because of preferential grazing.
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