Efficient management of irrigation water is fundamental in agriculture to reduce the
environmental impacts and to increase the sustainability of crop production. The availability of
adequate tools and methodologies to easily identify the crop water status in operating conditions
is therefore crucial. This work aimed to assess the reliability of indices derived from imaging
techniques\u2014thermal indices (Ig (stomatal conductance index) and CWSI (CropWater Stress Index))
and optical indices (NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical
Reflectance Index))\u2014as operational tools to detect the crop water status, regardless the eventual
presence of nitrogen stress. In particular, two separate experiments were carried out in a greenhouse,
on two spinach varieties (Verdi F1 and SV2157VB), with different microclimatic conditions and
under different levels of water and nitrogen application. Statistical analysis based on ANOVA
test was carried out to assess the independence of thermal and optical indices from the crop
nitrogen status. These imaging indices were successively compared through correlation analysis with
reference destructive and non-destructive measurements of crop water status (stomatal conductance,
chlorophyll a fluorescence, and leaf and soil water content), and linear regression models of thermal
and optical indices versus reference measurements were calibrated. All models were significant
(Fisher p-value lower than 0.05), and the highest R2 values (greater than 0.6) were found for the
regression models between CWSI and the soil water content, NDVI and the leaf water content, and
PRI and the stomatal conductance. Further analysis showed that imaging indices acquired by thermal
cameras (especially CWSI) can be used as operational tools to detect the crop water status, since no
dependence on plant nitrogen conditions was observed, even when the soil water depletion was
very limited. Our results confirmed that imaging indices such as CWSI, NDVI and PRI can be used
as operational tools to predict soil water status and to detect drought stress under different soil
nitrogen conditions
