Cotton significantly contributes to global agriculture and provides livelihoods for approximately 100 million farmers in 80 countries. Therefore, new approaches are needed to better inform producers, in near-real time, for optimising crop management practices, increasing profitability and sustainability. Here, we investigated the potential of proximal sensing metrics, derived from multispectral and thermal bands onboard an Unmanned Aerial Vehicles (UAVs), to estimate variability in cotton production due to different agronomic practices. We employed three main approaches, including (i) multilinear regression (MR), (ii) random forest (RF) and (iii) partial least square (PLS). All methods showed significantly strong relationship with lint yield. Specifically, the MR approach explained around 88% (R² = 0.88, RMSE = 322 kg/ha) of the variance in final yield across all plots. Further research is currently underway to explore the ability of multi-temporal, hyperspectral and radiative transfer models (RTM) to understand variability across different phenological stages in cotton management.