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This paper presents a method for mapping water stress in a maize field using hyperspectral remote sensing
imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an experimental
farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation
regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to measure
relative leaf water content (RWC), active chlorophyll fluorescence (DF/F0
m), leaf temperature (Tl) and
Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the
maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant physiological
status (DF/F0
m, difference between Tl and air temperature (Tair), and RWC) but not the canopy
structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the
Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band
(PRI570) showed the strongest relationships with DF/F0
m (r2 = 0.76), Tl Tair (r2 = 0.82) and RWC
(r2 = 0.64) and the red-edge Chlorophyll Index (CIred-edge) with LAI (r2 = 0.64). Thus PRI has been proven
to be related to water stress at early stages, before structural changes occurred.
A method based on an ordinal logit regression model was proposed to map water stress classes based
on airborne hyperspectral imagery. PRI570 showed the highest performances when fitted against water
stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map
water stress in the maize field. This study proves the feasibility of mapping stress classes using hyperspectral
indices and demonstrates the potential applicability of remote sensing data in precision agriculture for optimizing irrigation management.
