Evaluation of Terra/Aqua MODIS and Sentinel-2 MSI NDVI data for predicting actual evapotranspiration in Mediterranean regions. Issue 14 (17th July 2020)

• Record Type: Journal Article
• Authors: Maselli, Fabio; Angeli, Luca; Battista, Piero; Fibbi, Luca; Gardin, Lorenzo; Magno, Ramona; Rapi, Bernardo; Chiesi, Marta
• Is Part Of: International journal of remote sensing. Volume 41:Issue 14(2020)
• Page Start: 5186
• Page End: 5205
• Publication Date: 2020-07-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1731000
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Conventional meteorological data and remotely sensed Normalized Difference Vegetation Index (NDVI) images can be proficiently combined to predict actual evapotranspiration (ETA ) on different spatial and temporal scales. Up to now, however, the operational application of this approach in heterogeneous Mediterranean regions has found difficulty due to the insufficient spatial resolution of satellite sensors having high acquisition frequency (i.e. 250 m of Terra/Aqua Moderate Resolution Imaging Spectroradiometer, MODIS). The current study investigates the advantages brought for this objective by the recent availability of NDVI data taken from the Sentinel-2 MultiSpectral Instrument (MSI), which has a spatial resolution of 10 m. The investigation has been performed in two Mediterranean areas characterized by different spatio-temporal variability of vegetation cover. The first is a mountain coniferous forest, where such variability is low, while the second is a relatively small (around 10 ha) irrigated tomato field surrounded by other annual crops showing diversified growing cycles. An ETA estimation method based on NDVI data is applied in the two study areas and its performances are evaluated against ground references obtained through the elaboration of site measurements (i.e. meteorological, soil water content, and crop coefficient observations). The advantage of using MSI over MODIS NDVI images is marginal in the first case study, while is evident in the second. More particularly, such advantage is outstanding when the remote sensing method is applied in an operational mode, i.e. without using the information on the water supplied to the tomato crop by irrigation. This confirms that the utility of higher spatial resolution data is dependent not only on the fragmentation of the observed landscapes but also on the synchronicity of major vegetation growing cycles, which is influenced by both environmental and human-induced factors.