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 (I_g (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.

The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers’ income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past knowledge e.g., yield maps. Different approaches have been developed so far, based on the analysis of the within-field variability in crop and soil properties, but procedures were rarely suited for operational conditions. We identified here a low-inputs protocol to map management zones from soil electrical conductivity and/or crop vegetation indices, using a winter wheat field in northern Italy as a pilot case. The reliability of the alternative data sources was evaluated at three crop development stages using a yield map as reference. Red-edge and NIR (NDRE) bands were the most reliable data sources for management zones identification, with 62%, 68%, and 74% of correct classifications at early tillering, stem elongation, and late booting, respectively. Our work identifies a minimum dataset for accurate management zones’ definition and highlights that in-season monitoring based on the red-edge band was able to reliably identify management zones already at early tillering, despite minor differences in crop growth.

Cover crops are grown in order to provide agro-ecological services and must be terminated before planting the subsequent cash crop. Winterkill termination (by frost damage) depends on the interaction between crop frost hardiness, temperatures and the development stage reached at the time of sub-zero temperature exposure. Remotely sensing intensity, timing and spatial variation of cover crop frost damage can be useful for modeling and planning purposes. Therefore, in this study Sentinel-2 vegetation indices were employed in order to detect frost damage in four white mustard (Sinapis alba L.) fields located in Northern Italy. We estimated the starting date of frost events by means of vegetation indices (EVI, NDRE, NDVI, MMSR, and CCCI); we quantified and mapped frost damage at the sub-field level, using ground-based frost damage measurements carried out during the 2021/2022 season. As to frost damage quantification, MMSR outperformed the other VIs followed by CCCI and EVI (R² > 0.55). The adopted procedure to detect starting dates of frost events was successful in most cases, with a one-day and a four-day delay in the two best cases (NDRE). Finally, maps of frost damage were consistent with its observed spatial variation. We demonstrated that it is possible to employ vegetation indices in order to detect cover crop frost damage and thus assessing cover crop winterkill termination efficiency in the field. Further research is needed, involving additional field monitoring of white mustard in more diverse conditions, and extension of the calibration, as well as validation.