This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha⁻¹) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%N_a) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%N_c)) calculated through the dry mass at the time of the flight (W_flight). The inputs required to calculate the NNI (i.e., %N_a and W_flight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %N_a (R² = 0.59) and MTVI2 in estimating the W_flight (R² = 0.80). The %N_a and the W_flight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R² = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image.

Excess energy dissipation pathways (heat and fluorescence) were monitored, at leaf level as indicators of plant physiological status, with field spectroscopy techniques on poplar clones subjected to ozone fumigation. Measurements of spectral radiance emerging from a leaf provide a fast, non‐destructive method for the assessment of excess energy dissipation: xanthophyll‐related heat dissipation was estimated with the photochemical reflectance index (PRI) calculated from a traditional field spectrometer, and steady‐state fluorescence (Fs) under natural illumination conditions was estimated by exploiting a variation of the Fraunhofer line‐depth principle, where the radiance collected with very high resolution spectrometers (FWHM = 0.13 nm) was spectrally modelled. Both remotely‐sensed dissipation pathways responded to fumigation. During a 26‐day fumigation experiment, four diurnal cycles of spectral measurements were collected in parallel to meteorological and key physiological variables (active fluorescence, net photosynthesis) and leaf sample collection for pigment extraction. We outline evidence of a link between the remotely‐sensed Fs and PRI and leaf physiological status. These results open up new possibilities for assessment of plant stress by means of hyperspectral remote sensing.

High spectral resolution spectrometers were used to detect optical signals ofongoing plant stress in potted white clover canopies subjected to ozone fumigation. Thecase of ozone stress is used in this manuscript as a paradigm of oxidative stress. Steadystatefluorescence (Fs) and the Photochemical Reflectance Index (PRI) were investigatedas advanced hyperspectral remote sensing techniques able to sense variations in the excessenergy dissipation pathways occurring when photosynthesis declines in plants exposed to astress agent. Fs and PRI were monitored in control and ozone fumigated canopies during a21-day experiment together with the traditional Normalized Difference Vegetation Index(NDVI) and physiological measurements commonly employed by physiologists to describestress development (i.e. net CO₂ assimilation, active fluorimetry, chlorophyll concentrationand visible injuries). It is shown that remote detection of an ongoing stress through Fs andPRI can be achieved in an early phase, characterized by the decline of photosynthesis. Onthe contrary, NDVI was able to detect the stress only when damage occurred. These resultsopen up new possibilities for assessment of plant stress by means of hyperspectral remotesensing.