Forested environments are subject to large and high intensity unplanned fire events, owing to, among other factors, the high quantity and complex structure of fuel in these environments. Compiling accurate and spatially comprehensive fuel information is necessary to inform various aspects of land management in forested environments. Remote sensing may offer distinct advantages for this in comparison to traditional site-based approaches. We conducted a literature review of the past 10 years of research in the remote sensing of fire fuel in forested environments, with a focus on emerging methods of fuel estimation, and the fuel attributes estimated. We position our review of remote sensing research in relation to the fuel attributes that influence fire behaviour, as suggested by contemporary physics-based fire behaviour knowledge, and a summary of fuel inputs to widely applied forest fire behaviour models. We find a disconnect between recent remote sensing research and fuel characterisations relevant to contemporary fire behaviour knowledge. Specifically, we find a tendency in remote sensing research towards estimation of forest overstorey fuel attributes, and a relative lack of research that estimates more obscured, though highly relevant, fuel components such as understorey, surface, and bark fuel. We also find a tendency for recent remote sensing research to conceptualise fire fuel by existing fire behaviour models, with particular emphasis on matching pre-existing fuel model classifications. A case is made for remotely sensed forest fuel estimation grounded in current knowledge of fire behaviour processes and the fuel attributes known to influence these processes. Shortcomings in remote sensing of key forest fuel attributes are partly due to inherent limitations of current technologies, and we discuss recent and expected advancements in remote sensing research and technology that may drive significant future advancement in forest fuel estimation. Further, we suggest that recognition of interactions between fuel attributes and measurable biophysical forest properties can assist in addressing present limitations in remote sensing of key forest fuel attributes. Such process-based methods would be more spatially and temporally applicable, encourage new techniques for estimating fuel attributes using remote sensing data, and may encourage the development of fire behaviour and risk prediction systems that are more suited to remote sensing.