One decade (2011–2020) of European agricultural water stress monitoring by MSG-SEVIRI: workflow implementation on the Virtual Earth Laboratory (VLab) platform. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Bayat, Bagher; Montzka, Carsten; Graf, Alexander; Giuliani, Gregory; Santoro, Mattia; Vereecken, Harry
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 730
• Page End: 747
• Publication Date: 2022-12-31
• Subjects: ET -- SEVIRI -- ESI -- water stress workflow -- Europe -- VLab demonstration; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2061617
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content. However, dedicated efforts are still required to develop workflows, executable on cloud-based platforms, for ingesting the satellite data, performing the targeted processes, and generating the desired products. In this study, an operational workflow is proposed, based on monthly Evaporative Stress Index (ESI) anomaly, and implemented in cloud-based online Virtual Earth Laboratory (VLab) platform, as a demonstration, to monitor European agricultural water stress. To this end, daily time-series of actual and reference evapotranspiration (ETa and ET0 ), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, were used to execute the proposed workflow successfully on VLab. The execution of the workflow resulted in obtaining one decade (2011–2020) of European monthly agricultural water stress maps at 0.04˚ spatial resolution and corresponding stress reports for each country. To support open science, all the workflow outputs are stored in GeoServer, documented in GeoNetwork, and made available through MapStore. This enables creating a dashboard for better visualization of the results for end-users. The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.