The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), a standard level 2 European Space Agency (ESA) product, provides information on the chlorophyll content of vegetation (amount of chlorophyll per unit area of ground). This is a combination of information on Leaf Area Index (LAI, area of leaves per unit area of ground) and the chlorophyll concentration of those leaves. The MTCI correlates strongly with chlorophyll content when using model, laboratory and field spectrometry data. However, MTCI calculated with MERIS data has only been correlated with surrogate chlorophyll content data. This is because of the logistical difficulties of determining the chlorophyll content of the area covered by a MERIS pixel (9 × 104 m2). This paper reports the first attempt to determine the relationship between MTCI and chlorophyll content using actual MERIS data and actual chlorophyll content data. During the summer of 2006 LAI and chlorophyll concentration data were collected for eight large (> 25 ha) fields around Dorchester in southern England. The fields contained six crops (beans, linseed, wheat, grass, oats and maize) at different stages of maturity and with different canopy structures, LAIs and chlorophyll concentrations. A stratified sampling method was used in which each field contained sampling units in proportion to the spatial variability of the crop. Within each unit 25 random points were sampled. This approach captured the variability of the field and reduced the potential bias introduced by the planting pattern or later agricultural treatments (e.g. pesticides or herbicides). At each random point LAI was estimated using an LAI-2000 plant canopy analyser and chlorophyll concentration was estimated using a Minolta-SPAD chlorophyll meter. In addition, for each field a calibration set of 30 contiguous SPAD measurements and associated leaf samples were collected. The relationship between MTCI and chlorophyll content was positive. The coefficient of determination (R 2) was 0.62, root mean square error (RMSE) was 244 g per MERIS pixel and accuracy of estimation (in relation to the mean) was 65%. However, one field included a high proportion of seed heads, which artificially increased the measured LAI and thus chlorophyll content. Removal of this field from the dataset resulted in a stronger relationship between MTCI and chlorophyll content with an R 2 of 0.8, an RMSE of 192 g per MERIS pixel and accuracy of estimation (in relation to the mean) of 71%.