Estimating the effect of water shortage on olive trees by the combination of meteorological and Sentinel-2 data. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Battista, Piero; Bellini, Edoardo; Chiesi, Marta; Costafreda-Aumedes, Sergi; Fibbi, Luca; Leolini, Luisa; Moriondo, Marco; Rapi, Bernardo; Rossi, Riccardo; Sabatini, Francesco; Maselli, Fabio
• Is Part Of: European journal of remote sensing. Volume 56:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Sentinel-2 MSI -- rooting depth -- bilayer ecosystems -- soil water content; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2023.2194553
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Relative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly adapted to consider the deeper soil layer influential on olive trees. The current study investigates the modification of a recently proposed RSWC simulation method based on a combination of meteorological and satellite-derived normalized difference vegetation index (NDVI) data. The application of the method to an olive grove in Central Italy requires the estimation of both weather and NDVI contributions affecting solely olive trees, which is carried out through the use of appropriate data processing techniques. The RSWC estimates obtained reasonably reproduce the ground RSWC observations referred to the 1 m soil layer, which are representative of the WS affecting olive trees (r 2 = 0.795, RMSE = 0.15 and MBE = −0.09). The limits and prospects of this method are finally discussed with particular reference to the possible integration of the RSWC estimates within more complex ecosystem models.