Abstract
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—thermal indices (Ig (stomatal conductance index) and CWSI (Crop Water Stress Index)) and optical indices (NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index))—as 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.
Highlights
The knowledge of crop water status plays a role in many applications within biophysical, natural and agricultural research fields
The most used crop water stress index based on thermal data is the Crop Water Stress Index (CWSI), where the temperature difference between well-watered and dry canopy is normalized using a proxy of the evaporating power of the air above the crop [11]
This study focuses on the evaluation of the ability of thermal and optical imaging sensing applied to a spinach crop cultivated in greenhouse to detect crop water status under different nitrogen application levels
Summary
The knowledge of crop water status plays a role in many applications within biophysical, natural and agricultural research fields. Contrasting results were obtained, especially concerning the ability of the optical indices (e.g., Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI)) to identify the nature of different crop stresses [13,14] This is a relevant issue; different stressors may induce similar effects on the canopy optical properties in the VIS-NIR region [15]. Water and nutrients applications may be scheduled, and soil water content and plant nitrogen status may be accurately monitored All these factors contribute to make the greenhouse an optimal environment to investigate the effect of different types of stresses on spectral indices, and to check which indices are better suited to detect a certain type of stress [20]. Afterwards, the thermal and optical itnhdroicuegshwedreesctroumctpivareedanwdithndoinr-edcet smtreuacstuivremteencthsnoiqf ucreosp. wFiantaelrlys,tatcuosr,roelbattaiionnedatnharloyusgish danesdtrulicnteivaer arengdrensosnio-dnsesbtreutwcteiveentethcehrnmiqauleasn. dFinoapltliyc,acloirnrdeliacteisonanadnarleyfseirseanncde lmineeaasrurreegmreesnstisonwsebreetwcoenednuthcteerdmtaol aevnadluoaptteictahleinadbilcietys aonfdsprecfetrraelnicnedmiceasstuoremmoennittosrwthereeccronpdwuacterdsttoateuvsaalunadteptrheediacbt itlhitey corfosppwecatrtearl isntrdeiscse.s to monitor the crop water status and predict the crop water stress
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