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Th e chlorophyll content (Chl) of plants is an important variable in assessing their physiological status and photosynthetic performance. High-spectral-resolution refl ectance measurements have shown a powerful capacity to detect the subtle variations in spectral absorption features related to changes in Chl. Th is study proposed a simple technique based on an empirical linear relationship between Chl and the normalized area over refl ectance curve (NAOC) index to remotely estimate the spatial distribution of Chl. Th e NAOC is based on the calculation of the Chl-sensitive spectral interval obtained by hyperspectral measurements. Th e data sets used came from four recent European Space Agency campaigns that aimed to study crop properties from space: SPARC (Spain, 2003-2004), AgriSAR (Germany, 2006), CEFLES2 (France, 2007), and Sen3Exp (Spain, 2009). Th e following steps were undertaken to investigate the proposed relationship: (i) calibrating the SPAD-502 chlorophyll meter, (ii) collecting spatially distributed Chl data across various crops during the Sen3Exp campaign by using the calibrated SPAD-502 m, and (iii) fi tting a NAOC with Chl fi eld data and tuning it using data from the other campaigns so that a broad variety of crops, climatic conditions, and soil types was covered. Th e proposed technique was applied to multitemporal hyperspectral images during June 2009. Th e Chl maps were generated with a RMSE accuracy of 10 μg/cm 2 . Mapping their diff erences revealed the spatial distribution of greening up and senescing across crop fi elds. Th is new technique shows great potential for remotely monitoring the physiological status of crops.

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