Knowledge of the biochemical composition of the canopy is crucial to describing, understanding and predicting ecosystem functioning (Peterson et al., 1988; Curran et al., 1990; Matson et al., 1994). This is because major ecological processes that involve the exchange of matter and energy, e.g. photosynthesis, evapotranspiration, respiration and decomposition, are related to foliar biochemistry or nutritional status (Aber and Federer, 1992; Jacquemoud et al., 1996). Both macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium and sulphur) and micronutrients (iron, copper, manganese, zinc, boron and molybdenum) are important for growth and development of plants in this context (Marschner, 1995; Wang and Klinka, 1997). These nutrients are constituents of several bioorganic compounds including chlorophyll, proteins, lignin, cellulose and sugars (Mooney, 1986). Their deficiency or toxicity may affect growth and vitality of plants with notable symptoms including chlorosis (yellowing of plant tissue due to limitations on chlorophyll synthesis), necrosis (death of plant tissue), accumulation of anthocynanin resulting in a purple or reddish colours, and lack of new growth or stunted growth (Marschner, 1995). Remote sensing estimates of such leaf biochemicals can provide valuable information on ecosystem functioning, vitality and state over a wide range of spatial scales from the local to the global, e.g. as an indicator of ecosystem productivity (Peterson et al., 1988; Blackburn, 1998) or vegetation stress (Collins, 1978; Horler et al., 1983; Daughtry et al., 2000; Clevers et al., 2002). Many other applications of remote sensing of biochemicals are mentioned in the literature. An example includes early estimates of crop nitrogen concentration, which potentially could be useful in yield forecasting or proactive fertiliser application in precision agriculture (Haboudane et al., 2002; Goel et al., 2003), while other studies have shown that canopy nitrogen and phosphorus have an impact on migratory patterns of wildlife (McNaughton, 1988). Curran (2001) defined ecological questions that address the condition of vegetation as third-level questions in contrast to firstand second-level questions that consider the type and amount of vegetation, respectively. Third-level ecological questions often require assessment of solar energy–target interactions using narrow and contiguous spectral measurements (Curran et al., 2001). However, the Remote sensing estimates of leaf biochemicals provide valuable information on ecosystem functioning, vitality and state at local to global spatial scales. This paper aims to give an overview of the state of the art of foliar biochemistry assessment in general and, where possible, attention is given to: (1) Eucalyptus forest environments, (2) use of hyperspectral remote sensing or imaging spectroscopy, and (3) the challenges towards operational application of such assessments. Estimation of foliar biochemicals has improved significantly from early broad-band sensor attempts, given the advent of hand-held, airborne and space-borne spectrometers. These instruments provide sensing in contiguous, narrow spectral bands in the visible to shortwave infrared, as compared to the small number of broad spectral bands provided by multispectral sensors. Chlorophyll, nitrogen, cellulose and lignin represent a sample of biochemicals that have been assessed successfully, particularly at leaf level and with varying success at the canopy scale. A major challenge is scaling of predictions of biochemicals from ground to airborne and ultimately space-borne levels. This entails development of algorithms that minimise the contributions of canopy structure, atmospheric conditions, sensor/illumination geometry and leaf water content variations. Some advances have been made in this direction including the derivation of new vegetation indices and the use of spectral transformations such as derivative analysis and continuum removal. Other studies have focused on developing physically based models, e.g. radiative transfer models (RTMs), which appear to be more robust when compared to statistical models. However, the application of RTMs needs to progress beyond the estimation of only chlorophyll and biochemicals in monoculture environments to other nutrients and adapted for more complex canopies. Furthermore, inversion techniques of these models need to be improved.