A Fast Regression-Based Approach to Map Water Status of Pomegranate Orchards with Sentinel 2 Data

Abstract

Midday stem water potential (Ψstem) is an important parameter for monitoring the water status of pomegranate plants and for addressing irrigation management. However, Ψstem ground surveys are time-consuming and difficult to carry out periodically over vast areas. Remote sensing, specifically Copernicus Sentinel 2 data (S2), offers a promising alternative. S2 data are appropriate for Ψstem monitoring due to their geometric, temporal and spectral resolutions. To test this hypothesis, two plots were selected within a pomegranate orchard in southern Italy. A pressure chamber was used to collect Ψstem measurements on four days in summer 2021. Ground data were compared with the temporally closest S2 images with the aim of testing the effectiveness of remotely sensed imagery in estimating and mapping the Ψstem of pomegranate plants. Regression models were applied with a limited number of ground observations. Despite limited ground observations, the results showed the promising capability of spectral indices (NDVI, NDRE and NDWI) and S2 bands in estimating (MAE ≅ 0.10 MPa and NMAE < 10%) Ψstem readings. To understand the dimensional relationship between S2 geometric resolution and the orchard pattern, predictive models were tested on both native S2 data and on denoised (unmixed) data, revealing that native data are more effective in predicting Ψstem values.