Eirini Trypidaki
Predoctoral Researcher at CREAF and member of the Methods and Applications in Remote Sensing and Geographic Information Systems (GRUMETS) research group. Her work focuses on the development and refinement of methodologies for high-resolution drought monitoring, with an emphasis on advancing the operational use of environmental data. This research is closely aligned with the objectives of the Open Earth Monitor (OEMP) project and highlights the practical applications of high-resolution Earth observation in sectors such as agriculture, insurance, and reinsurance.
Sessions
Drought is a natural hazard caused by a precipitation deficit and consequent hydrological imbalance (Pachauri et al., 2014; Trenberth et al., 2014), with significant economic and environmental impacts, particularly in agriculture and forests. Although, ground-based observations provide high accuracy for drought-related parameters such as precipitation, temperature, and soil moisture, they lack in coverage and cost, making them unsuitable for large-scale, high-resolution assessments. In contrast, remote sensing technologies offer a cost-effective alternative, providing continuous spatial information over large regions.
This study is conducted as part of the Open Earth Monitor (OEMC) project, which aims to develop a global, high-resolution system for drought monitoring. Our research focuses on identifying and improving existing approaches to create high-resolution monthly drought maps by exploiting drought indicators from ground station meteorological data and remotely sensed soil moisture. Soil moisture plays a key role in drought monitoring and prediction, especially in water-limited ecosystems (D'Odorico et al., 2007; Moran et al., 2004; Peters-Lidard et al., 2008), such as the Ebro Basin at northeast of Spain, the study area.
In terms of the available soil moisture datasets, existing datasets often lack the resolution and reliability required for an effective assessment. To address this, a thorough review was conducted, various soil moisture products provide global coverage, but their coarse spatial resolution requires downscaling techniques to improve usability at local and regional scales. Since our focus is on developing a drought monitoring system with an agricultural emphasis, we prioritized products with spatial resolution 1km. A recent review (Brocca et al., 2024) on soil moisture products in Italy demonstrated that Sentinel-1 products show good agreement in terms of drought detection. Considering that drought is a long-term phenomenon, a minimum timescale is necessary for meaningful anomalies detection. However, high-resolution soil moisture data are available for a shorter period than meteorological data, that span from 1950 till today.
Based on these, we selected two high-resolution datasets: the Sentinel-1 dual-polarization SAR (DPA) with a 1 km spatial resolution (Fan et al., 2025), and downscaled SMOS soil moisture data at 1 km resolution (Escorihuela et al., 2018; Merlin et al., 2013) (), provides a longer temporal record. Our analysis compares these data sets across timescale to determine whether soil moisture data compliment the drought monitoring approaches.
() The SMOS dataset used in this work was produced within the ACCWA project which has received funding from the European Union's H2020-MSCA-RISE-2018 programme under grant agreement No. 823965."