Abstract

Irrigation management of drip-irrigated olive orchards is usually evaluated using the midday stem water potential (?stem) measured using a pressure chamber. However, this methodology is time-consuming and labor-intensive. In this regard, remote sensing tools based on spectral reflectance (SR) indices have become an attractive alternative for ?stem monitoring. This study aimed to develop and validate regression models based on SR indices to simulate the ?stem of a drip-irrigated superintensive olive orchard located in Pencahue Valley, Maule region of Chile. ?stem and SR data were obtained from two independent irrigation experiments carried out during the 2013–14 and 2014–15 growing seasons. ?stem was measured using a Scholander-type pressure chamber, while SR data were collected using a field-portable spectrometer. Results indicated that the coefficients of determination (r2) were between 0.62 and 0.65 for the linear models based on photochemical reflectance (PRI), maximum difference water (MDWI), normalized difference infrared (NDII), and moisture stress (MSI) indices. Validation indicated that these models predicted ?stem with mean absolute error (MAE), root mean square error (RMSE), and index of agreement (d) between 0.63–0.73, MPa, 0.79–0.92 MPa, and 0.85–0.88, respectively. In this case, the MSI-based model presented the lowest MAE and RMSE, and the highest d. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.