The use of remotely sensed imagery for the monitoring of both plant biodiversity and functional traits in grassland ecosystems has increased substantially in the last few decades. More recently, uncrewed aerial vehicles (UAVs) have begun to play an increasingly important role, providing repeatable very high-resolution data, acting as a bridge between the decameter satellite imagery and the point scale data collected on the ground. At the same time, machine learning approaches are rapidly expanding, adding new analysis and modelling tools to the plethora of UAV, aircraft and satellite observational data. Here, we provide a review of remotely sensed monitoring methods for grassland plant biodiversity and functional traits (Leaf Dry Matter Content, Crude Protein, Potassium, Phosphorous, Nitrogen and Leaf Area Index) between 2018 and 2024. We highlight the key innovations that have occurred, sources of error identified, new analysis methods presented and identify the bottlenecks to and opportunities for further development. We emphasise the need for (1) the integration of observations across spatial and temporal scales, (2) a more systematic identification and examination of sources or error and uncertainty (3) more widespread use of hyperspectral satellite data and (4) greater focus on the development of grassland global spectra, species and traits data base, from multi- and hyper-spectral instruments, to accelerate the creation of more robust, scalable and generalisable remote sensing based grassland models.